About the webinar
Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products.
Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc. are employing AI-powered solutions to detect defects early and avoid the defective products going live.
Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives.
What you will learn
- How various industries are leveraging AI to assist in visual inspections.
- Live Demo: How to collect data, label and train the AI model to detect defects, all within a few minutes.
- Address the challenges of AI & Machine learning and how to overcome them.
Introduction to reverse engineering with the concept of re-engineering in the context of software engineering. It includes introduction to reverse engineering, historical background of reverse engineering, forward engineering vs reverse engineering, process of reverse engineering and real life example of reverse engineering now-a-days.
Introduction to reverse engineering with the concept of re-engineering in the context of software engineering. It includes introduction to reverse engineering, historical background of reverse engineering, forward engineering vs reverse engineering, process of reverse engineering and real life example of reverse engineering now-a-days.
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
This PPT gives you more than enough introduction to artificial intelligence and makes you to learn yourself artificial intelligence creating interest upon it
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
In this slide I answer the basic questions about machine learning like:
What is Machine Learning?
What are the types of machine learning?
How to deal with data?
How to test model performance?
A complete illustrated ppt on 3D printing technology. All the additive processes,Future and effects are well described with relevant diagram and images.Must download for attractive seminar presentation.3D Printing technology could revolutionize and re-shape the world. Advances in 3D printing technology can significantly change and improve the way we manufacture products and produce goods worldwide. If the last industrial revolution brought us mass production and the advent of economies of scale - the digital 3D printing revolution could bring mass manufacturing back a full circle - to an era of mass personalization, and a return to individual craftsmanship.
Twitter Sentiment Analysis in 10 Minutes using Machine LearningSkyl.ai
About the webinar:
Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook.
This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
What you will learn
- How businesses are leveraging sentiment analysis to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: How to build a twitter sentiment analysis model
How to classify documents automatically using NLPSkyl.ai
About the webinar
Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business.
Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts.
In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc.
What you will learn
- How businesses are leveraging document classification to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: Classify news articles into the right category using convolution neural network
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
This PPT gives you more than enough introduction to artificial intelligence and makes you to learn yourself artificial intelligence creating interest upon it
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
In this slide I answer the basic questions about machine learning like:
What is Machine Learning?
What are the types of machine learning?
How to deal with data?
How to test model performance?
A complete illustrated ppt on 3D printing technology. All the additive processes,Future and effects are well described with relevant diagram and images.Must download for attractive seminar presentation.3D Printing technology could revolutionize and re-shape the world. Advances in 3D printing technology can significantly change and improve the way we manufacture products and produce goods worldwide. If the last industrial revolution brought us mass production and the advent of economies of scale - the digital 3D printing revolution could bring mass manufacturing back a full circle - to an era of mass personalization, and a return to individual craftsmanship.
Twitter Sentiment Analysis in 10 Minutes using Machine LearningSkyl.ai
About the webinar:
Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook.
This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
What you will learn
- How businesses are leveraging sentiment analysis to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: How to build a twitter sentiment analysis model
How to classify documents automatically using NLPSkyl.ai
About the webinar
Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business.
Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts.
In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc.
What you will learn
- How businesses are leveraging document classification to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: Classify news articles into the right category using convolution neural network
Ai in insurance how to automate insurance claim processing with machine lear...Skyl.ai
Explore more at https://skyl.ai/form?p=start-trial
About the webinar
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
How to analyze text data for AI and ML with Named Entity RecognitionSkyl.ai
About the webinar
The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data
What you will learn
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
How to analyze text data with Named Entity RecognitionSkyl.ai
The internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents, analyze sentiments, improving automatically generated summaries, etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data.
What you will learn:
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
Future of Ecommerce: How to Improve the Online Shopping Experience Using Mach...Skyl.ai
About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you will learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
AI for Customer Service - How to Improve Contact Center Efficiency with Machi...Skyl.ai
About the webinar
It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity.
Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value.
What you'll learn:
- How organizations are leveraging AI & Machine learning in Customer Service
- Live Demo of AI & ML in Customer Service
- Best practices to automate machine learning models
To explore more, visit: https://skyl.ai/form?p=start-trial
Static Testing: We Know It Works, So Why Don’t We Use It?TechWell
We know that static testing is very effective in catching defects early in software development. Serious bugs, like race conditions which can occur in concurrent software, can't be reliably detected by dynamic testing. Such defects can cause a business major damage when they pop up in production. Despite its effectiveness in early defect detection and ease of use, static testing is not very popular among developers and testers. Meena Muthukumaran discusses reasons why static testing is not commonly used or not used optimally: lack of awareness, lack of time, and myths about cost and effort requirements. Meena explains ways to perform effective static testing—identifying your needs, shortlisting the tools based on your needs, creating awareness and a culture for proactively eliminating defects early in the lifecycle, and encouraging effective usage of static testing. She offers various implementation solutions to suit different development methodologies and ways to measure the benefits realized with static testing.
No Code AI - How to Deploy Machine Learning Models with Zero Code?Skyl.ai
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics. The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives.
Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects.
What you'll learn
- Traditional vs No Code AI Process
- Best practices to accelerate machine learning adoption
- Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...Skyl.ai
About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you'll learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
AI in Quality Control: How to perform Visual Inspection with AISkyl.ai
About the webinar:
Recalls are a manufacturer’s nightmare. Failure to detect and resolve a quality problem that results in a recall costs the business millions of dollars every year, not to mention the brand damage and reputation cost. In some cases, defects can even endanger human lives when it comes to construction, food, airline, or healthcare products.
Leading manufacturers in the food industry, consumer goods, electronics, or any other production line, as well as industries like construction, utilities, etc., are employing AI-powered solutions to detect defects early and avoid defective products going live.
Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
Through this webinar, we will learn how AI and Computer Vision can be used to aid visual inspections and efficiently detect defects to prevent huge money or losses to human lives.
What you will learn:
- How various industries are leveraging AI to assist in visual inspections.
- Live Demo: How to collect data, label, and train the AI model to detect defects, all within a few minutes.
How to do Secure Data Labeling for Machine LearningSkyl.ai
About the webinar
Data annotations or more commonly called data labeling is an integral part of AI and Machine learning.
One of the biggest concerns that organizations have while doing AI and ML is about handing data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling
- Live demo of a secure data labeling platform
How an AI-backed recommendation system can help increase revenue for your onl...Skyl.ai
About the webinar
Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick.
Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers.
This webinar will showcase a live demo of how to build such a robust recommendation model in hours.
What you will learn
- How e-commerce companies drive sales through AI-powered product recommendation engines
- Challenges faced in ML automation and how to overcome those using a unified ML platform
- Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform
Manufacturing, a slow-adopter of Analytics, is now catching up in leaps and bounds. Across all business domains, applying analytics is providing answers to the most critical questions of the business.With exponential expansion of data, data driven insights have become a strategic necessity.
This booklet explores a few use cases of Big Data for manufacturing and how it can be leveraged.
For more info visit: https://www.teamcomputers.com/businessanalytics/Manufacturing/Booklet-Manufacturing-Digital.pdf
AI in Insurance: How to Automate Insurance Claim Processing with Machine Lear...Skyl.ai
About the webinar
Insurance companies are looking at technology to solve complexity created by presence of cumbersome processes and presence of multiple entities like actuaries, support team and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
What you'll learn
- How Insurance companies are using ML to drive more efficiency and business gain
- Best practices to automate machine learning models
- Demo: A deeper understanding of the end-to-end machine learning workflow for car damage recognition using Skyl.ai
AI for Customer Service: How to Improve Contact Center Efficiency with Machin...Skyl.ai
About the webinar
It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost, and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity.
Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat, and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value.
What you will learn
- How organizations are building engaging interactions that deliver value to customers
- Best practices to automate AI/ML models
- Demo: How to route customer queries to the right department or professional
With shrinking production cycles, increasing demand for customized products, and a growing skills gap in the workforce, there are many pressures affecting the manufacturing industry. Technology offers many potential solutions, along with its own set of changes and challenges, including data overload.
Advanced analytics solutions can help address these issues. Some enterprises are already reaping the benefits, like automated supply chains and predictive maintenance, but often it’s unclear where to begin.
Learn how manufacturing analytics solutions can improve core production and supply chain operations like quality assurance and inventory optimization. With the right approach and tools, and using your existing technology investments, you can uncover potential insights and solutions in the information you already have.
Similar to AI in Quality Control: How to do visual inspection with AI (20)
How to perform Secure Data Labeling for Machine LearningSkyl.ai
Data annotations or more commonly called data labeling are an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost, and quality in data labeling
How to do Secure Data Labeling for Machine LearningSkyl.ai
Data annotation or more commonly called data labeling is an integral part of AI and Machine Learning.
One of the biggest concerns that organizations have while doing AI and ML is about handling data.
Many organizations have concerns about data security and privacy of the training data, especially highly regulated industries like Healthcare, Banking, Government, etc. where data privacy and security are paramount.
What you will learn:
- Risks associated with data annotations and how to manage data privacy and data protection
- How to handle deployments and infrastructure to manage data security
- How to manage collaborative contributors for secure data labeling to balance scale, security, cost and quality in data labeling
- Live demo of a secure data labeling platform
No Code AI - How to Deploy Machine Learning Models with Zero Code?Skyl.ai
In the past, getting insights from the data using machine learning (ML) and artificial intelligence (AI) required experts with coding skills and knowledge of math & statistics.
The scarcity of talent and huge infrastructure set up cost, often makes it difficult for organizations to get early results from their Machine Learning initiatives. Through this webinar, we will learn how 'No Code AI' tools make it possible to leverage the power of machine learning without needing to code. It is helping business analysts, domain experts, and business decision-makers to experiment and get started with quick-win Machine Learning projects.
What you will learn:
- Traditional vs No Code AI Process
- Best practices to accelerate machine learning adoption
- Demo: How organizations are deploying machine learning models without coding expertise within hours, not weeks
AI in Insurance: How to Automate Insurance Claims Processing with Machine Lea...Skyl.ai
Insurance companies are looking at technology to solve complexity created by the presence of cumbersome processes and the presence of multiple entities like actuaries, support teams, and customers in the claim processing cycle.
Today, a lot of insurance companies are opting for Machine Learning to simplify and automate the processes to reduce fraudulent claims, predict underwriting risks, improve customer relationship management. This automated insurance claim process can remove excessive human intervention or manual errors and can report the claim, capture damage, update the system, and communicate with the customers by itself. This leads to an effortless process enabling clients to file their claims without much hassle.
In this webinar, we will discuss how insurers are increasingly relying on machine learning to improve claim processing efficiency and increase ROI.
What you will learn:
. Deep dive into how insurance companies are adopting AI
. Discuss prominent industry use cases
. Live demo of vehicle damage assessment for insurance claims management
Solving the dilemma should you build or buy aiSkyl.ai
About the webinar
Long gone are the days of questioning if your organization requires Artificial Intelligence to drive competitive advantage. 84% of businesses say AI will enable them to obtain or sustain the competitive advantage [Forbes].
AI offers highly data-driven insights, automates mundane processes, enhances customer experience, and hence increases overall efficiency. 36% of executives say their primary goal for AI is to free up workers to be more creative by automating tasks [HBR].
Businesses know what AI solutions they need but the real challenge lies in getting them implemented. AI initiatives require proper evaluation of the organizations’ ability to build in-house AI technology or buy commercially available AI applications.
Through this webinar, you will know the factors to consider while making the decision of AI implementation, and you get the answer to your biggest question - whether to build or buy AI application.
What you will learn
What factors to evaluate before making a decision to build or buy an AI solution
What will you require to build an AI model specific to your organizational need
How does building an AI solution fit into the long-term business model and help in gaining competitive advantage
How AI and Machine Learning can Transform OrganizationsSkyl.ai
About the webinar
83% of businesses say AI is a strategic priority for their businesses today, while only 23% of businesses have incorporated AI into processes and product/service offerings today [source: Forbes].
Artificial intelligence and machine learning have started to disrupt the traditional way of doing business and revolutionize everything from farming to rocket science. Do you want to be left behind?
Through this webinar, we will discover how various industries are adopting technologies to innovate and disrupt their business models to increase revenue, reduce costs, improve quality and customer satisfaction as well as to handle risks.
What you will learn
- How organizations have gained benefits with AI in their business to increase revenue, reduce cost, improve quality and manage risks
- Mind-blowing Innovative and disruptive emerging AI use cases in various industry sectors
- How to leverage AI in your business to get a competitive advantage
AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions.
Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you will learn:
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
AI in Healthcare: Can AI Help in Diagnosing CoronavirusSkyl.ai
About the webinar
The entity that has caused a newfound global love of hand sanitizers and masks? The Coronavirus (known as ‘2019-nCov’ or ‘Covid-19), which has infected about 5,00,000 people globally within a few months!
According to the WHO: 'In the most severe cases, the infection can cause pneumonia, severe acute respiratory syndrome, and even death.' Statements like these beg the question: 'How accurate are the tests to spot the disease?' 'Can AI assist in giving a more accurate diagnosis?'
The AI Model generated via Skyl.ai’s deep learning platform can accurately detect COVID-19 through patterns in X-ray scans and differentiate it from community-acquired pneumonia and other lung diseases that may otherwise be overlooked by a doctor.
Through this webinar, we will demo how AI can be used to test the Covid19 infections, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions can leverage AI to detect COVID-19 and reduce the time taken to provide critical care to patients who are affected.
- Discuss the approach to automate the machine learning workflow, creating and deploying models in hours and not weeks or months.
- Demo: How to create an ML model that can detect COVID-19 from chest x-rays using Skyl.ai.
How AI is Changing Medical Imaging in the Healthcare Industry Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
Twitter Sentiment Analysis in 10 Minutes Using Machine LearningSkyl.ai
About the webinar
Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook. This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!
Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
What you'll learn
- How businesses are leveraging sentiment analysis to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: How to build a twitter sentiment analysis model
How to Build an AI-powered Automatic Document Classification ModelSkyl.ai
About the webinar
Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business. Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts.
In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc.
What you'll learn
- How businesses are leveraging document classification to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: Classify news articles into the right category using convolution neural network
How to Implement Biomedical Named Entity Recognition with Machine Learning Skyl.ai
Biomedical research & healthcare practices are generating information like scientific publications, transcription and EMR records in an unprecedented way. For example, the new generation of sequencing tech is helping to process billions of DNA sequence data per day. Further, Biomedical vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively tag, index and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how Machine Learning can be used to automate complex processes and help in extracting key entities like the chemicals, diseases, genes, proteins, anatomical constituents, organization name, etc.
What you'll learn
- How organizations are leveraging Machine Learning in biomedical & healthcare industry
- Best practice to automate machine learning models in hours not months
- Live demo - Identify & classify complex medical terms & names with NERC
AI Recruitment - How Businesses Are Winning the Race for the TalentSkyl.ai
About the webinar
Have you ever faced this situation wherein your recruitment team didn’t get enough time to build a stellar candidate experience and faced a hard time sifting through thousands of resumes and scheduling calls?
According to a survey by HR.com, in today's time one in ten recruiters use AI and nearly half expect to adopt it in their recruitment process within the next 5 years to keep up with changing market pace.
Over the course of 45 minutes, you will gain insights into how AI is changing recruitment and giving companies a competitive edge.
What you'll learn:
- How organizations are leveraging AI to accelerate the search for top talent
- Live Demo of smart resume search using Natural language processing
- Best practice to automate machine learning models in hours not months
To explore more, visit: https://skyl.ai/form?p=start-trial
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
To explore more, visit: https://skyl.ai/form?p=start-trial
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
AI in Quality Control: How to do visual inspection with AI
1. AI in Quality Control: How to do
visual inspection with AI
2. Technology enthusiast with 13+ years of experience working
in the information technology and services industry. Leads
cutting-edge solutions for businesses using Machine Learning
and Artificial Intelligence.
Areas of expertise includes Architecture design, Solutioning,
Data Engineering and Deep Learning.Mohit Juneja
Solutions Architect
The Speaker
3. Extensive experience building future tech products using
Machine Learning and Artificial Intelligence.
Areas of expertise includes Deep Learning, Data Analysis,
full stack development and building world class products
in ecommerce, travel and healthcare sector.
Shruti Tanwar
Lead - Data Science
The Speaker
4. Bikash Sharma
CTO and Co-founder at
Skyl.ai
CTO & Software Architect with 15 years of experience
working at the forefront of cutting-edge technology
leading innovative projects
Areas of expertise include Architecture design, rapid
product development, Deep Learning and Data Analysis
The Panelist
5. Getting familiar with ‘Zoom’
All dial-in participants will be muted to enable the presenters
to speak without interruption
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
6. Live Demo of
detecting cracks in
cement surfaces
using Machine
learning
How industries are
leveraging AI to
assist in visual
inspection
How to quickly
overcome the
challenges in
building ML models
1 2 3
...In the next 45 minutes
7. Machine Learning automation platform for unstructured data
A quick intro about Skyl.ai
Guided Machine Learning Workflow
Build & deploy ML models faster on
unstructured data
Collaborative Data Collection & Labeling
Easy-to-use & scalable AI SaaS platform
8. POLL #1
At what stage of Machine learning adoption your
organization is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business
10. Food and Beverage Industry
Foreign object detection - detailed
inspection of incoming food materials
on the production line
Defective Labeling Identification -
Identify misbranding, incorrect
positioning and damaged labels to
ensure the product conformity
11. Automotive Industry
Imperfect Surface Detection -
Spot the visible irregularities on
the product to improve quality of
the output
Inspect Automobile parts -
detection of missing nuts and bolts
in the automobile under production
12. Pharmaceuticals and Medical Industry
Vial/Phial cap and liquid level
Detection - detect missing caps and
check the liquid levels in the vials used
for clinical diagnostic
Quality check of Face Masks -
detection of missing facemask
components and damages to ensure the
produced masks pass ISO standards
13. Construction Industry
Concrete crack detection - detection
of surface cracks during monitoring
and inspection of civil engineering
structures
Spall detection - detects damages
and segment the intact region to
measure the spall depth
14. Benefits of Visual Inspection for Quality Control
Increased
Accuracy of
Final Goods
Reduced
Quality
Control
Downtime
Reduced
Costs of
Quality
Checking
Improved
Production
Efficiency
18. Skyl.ai - as ML automation platform
Efficient
Data Management
Solve your data issues; collect and manage data
efficiently
Accuracy
& Quality
Maintain accuracy and quality; train and test faster;
monitor quality
Effective
Collaboration
Collaborate and manage projects efficiently
Early
Visibility
Get early visibility; visualize and affirm correctness
on every step of the way
Scalable
High - Performance
Access on-demand and scalable, high-performance
infrastructure
Reduce
Cost
Reduce cost of implementation; do it with less
specialized resources
19. POLL #2
Some challenges that you are facing while
implementing AI & Machine Learning
⊚ Not started yet, so no challenges
⊚ Data collection
⊚ Data Labeling
⊚ Large volumes of data
⊚ Identifying the right data set to
train
⊚ Data Security
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right algorithms
20. Overcoming the AI / ML
challenges with the right
tools and technologies03
21. Best Practices for Data Collection
⊚ Use relevant data sources for data
collection
⊚ Establish proper data collection
mechanisms
⊚ Do not stop with too-small data sample
size
Data Collection
Data Quality
Data SecurityData Security
Data SecurityData Labeling
22. Best Practices for Data Quality
⊚ Do validate your data and data sources
⊚ Clean up your data regularly - “garbage
out”
⊚ Data correction - remove duplicates,
missing data, etc
⊚ Check the consistency of data while data
acquisition
Data Collection
Data Quality
Data SecurityData Security
Data SecurityData Labeling
23. Best Practices for Data Security
⊚ Monitor data processes continuously to
mitigate risks
⊚ Increase data security with encryption and
tokenization
⊚ Controlled access flows with different
organizational roles
Data Quality
Data Collection
Data SecurityData Security
Data SecurityData Labeling
24. Best Practices for Data Labeling
⊚ Define the problem you want to solve and
use relevant labels inline with the entities
you want to predict
⊚ Analyse trends and progress of your data
labeling in real time - to find biases
⊚ Do not add new entity types midway
⊚ Use short tag lists and annotationsData Labeling
Data Quality
Data SecurityData Security
Data Collection
25. Challenges
⊚ Requisite Infrastructure
⊚ Cost of Infrastructure
⊚ Data and ML pipeline
⊚ Model at scale for
inference
Best Practices
⊚ Use SaaS Model (Pay as you go) -
reliable, scalable and secure
⊚ The right software tuned and
optimized to fit the underlying hardware
⊚ A flexible infrastructure that can be
deployed in the cloud or in an on-premise
data center to optimize performance
Technology issues and solutions
26. Best Practices
⊚ Train existing employees with
education related to AI and ML
⊚ Use Saas products with good
documentation, support and
implementation that alleviates the need
to have highly skilled data scientists and
resources with multiple skills.
40%
Lack of skilled talent
Source: Techrepublic
Barrier in adopting AI
⊚ Companies face
shortage of necessary
in-house talent.
Specialized skills and knowledge
27. Challenges
⊚ Long implementation time
⊚ Measure ROI of the AI
deployment
Best Practices
⊚ AI implementation results in
increased process efficiency and
automation.
⊚ Create own AI KPIs and analyze
the difference in the
measurements before and after AI
deployment.
TechRepublic claims that 56%
of global CEOs expect it to take
3-5 years to see any real ROI on
their AI investment.
Speed and time to market
28. Collect
Feedback
Monitor the
model
Process
Feedback
Deploy the
changes
Train and
Evaluate
Continuous
Improvement
Best Practices
⊚ Perform incremental and
measurable improvements
⊚ Monitor your deployed models
and analyse inference count,
accuracy and execution time.
⊚ Check model performance in
real time
Monitoring and continuous improvement
29. AI Project Management
More Challenges and
Concerns
⊚ Project Cost
⊚ Return on Investment
⊚ On-demand scalability
⊚ Iterative corrections in
AI project
Source: AI for People and Business: A Framework for Better Human Experiences and Business Success
DATA
Time Cost
Performance Requirements
The TCPR Model
30. Skyl.ai - as ML automation platform
Efficient
Data Management
Solve your data issues; collect and manage data
efficiently
Accuracy
& Quality
Maintain accuracy and quality; train and test faster;
monitor quality
Effective
Collaboration
Collaborate and manage projects efficiently
Early
Visibility
Get early visibility; visualize and affirm correctness
on every step of the way
Scalable
High - Performance
Access on-demand and scalable, high-performance
infrastructure
Reduce
Cost
Reduce cost of implementation; do it with less
specialized resources
31. ⊚ Free 1 month Trial + POC
⊚ Complimentary 30 min consultation
⊚ AI Implementation Playbook
www.skyl.ai contact@skyl.ai
Special offer for you...
33. We hope to hear from you soon
Thank you for joining!
85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
Editor's Notes
Hello everyone and welcome. Thank you for joining today’s webinar on AI in Quality Control: How to do visual inspection with AI. My name is Edwin Martinez and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
First we have Mohit Juneja, Mohit is a Solutions Architect and Technology supporter with over 13 years of experience in the IT and Service industry. He leads cutting-edge solutions for businesses using Machine Learning and AI. He’s an expert in Architect design, Data Engineering, and Deep Learning. Welcome Mohit!
Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI.
Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
Finally, we have Bikash Sharma joining us today.
Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions.
He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
Before we begin, I’d like to briefly talk about some Zoom features that will be relevant to us.
All participants in the webinar will be muted to avoid any interruptions during the session.
Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen.
We’ll make sure to address your questions during the Q&A session.
Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again.
So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Mohit
Exploring - Curious about it
Planning - Creating AI/ML strategy
Experimenting - Building proof of concepts
Scaling up - Some departments are using it
In production - Using it in product features
Transforming - AI/Ml driven business
In the automotive and automotive-parts industries, the production rate, manufacturing environment, and composition of raw materials all markedly affect the quality and yield of the final product. As a result, auto manufacturers have been early adopters of machine-vision systems
Defective Labeling Identification: The food production chain includes several crucial steps requiring close inspection of a product. One of them is checking if the label is correct. Misbranding, incorrect positioning and damaged labels must be avoided to ensure the product conforms to current Good Manufacturing Practices.
Foreign object detection: In a production line or during packaging, very detailed assumptions about the incoming material are made. Foreign objects being conveyed into a machine might destroy the machine or, when packaged and delivered harm or at least annoy the consumer. Thus, the detection of any foreign object is necessary in order to take respective actions. In the figure above, you see a stone that was erroneously harvested together with apples and that needs to be separated before any subsequent treatment.
Imperfect surface detection: To guarantee the right level of quality of a manufactured item, we detect any visible irregularities. In addition, we not only compute metrics such as the shape or the extent of a defect, but also apply classification with regard to its type. While certain types of defects might be tolerable, others will be blockers. Also this information helps to identify the cause of the defect.
Inspect Automobile: This has become a primary mission for many machine vision systems on automotive industry production lines. These vision systems use powerful pattern recognition capabilities to find missing material, chips, scratches, dents, misplaced markings, and a wide variety of other flaws. In addition to ensuring the quality of finished parts and products, they also enable manufacturers to reduce costs by eliminating defective pieces before wasting additional material and production time on them.
Clinical diagnostic vision technology can provide improved system ease of use, intelligence and error proofing to set your systems apart from the competition.(Test tube cap presence/absence, Test tube identification by shape, Proper system set up through object recognition, Test tube size verification by width, Micro-array tray identification / barcode reading, Liquid level detection)
By leveraging both machine vision and deep learning technology manufacturers can ensure masks are produced in compliance with ISO standards and catch defective masks before they are shipped. vision system detects the presence of facemask components such as earbands and strap welds, while also measuring the width of the masks to ensures they are manufactured to the correct size.
The manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. Manual inspection can also be difficult to perform in case of high rise buildings and bridges.
Increased Accuracy: CV-based approaches ensure a higher grade of accuracy within the accepted tolerance in every manufacturing process. Even when workers use specific equipment, such as a magnifying glass, machines are still more precise.
Reduced Downtime: An automated system is an effective tool to reduce quality control downtime. As the system is fully automated, it runs much faster, it is able to work 24/7 and it does not need any breaks for rest.
Improved Efficiency: Visual inspection can improve the production efficiency. These systems can catch errors at a faster rate. Analysis of these observed defects can be made quickly and necessary corrections can be made quickly.
Reduced Costs: An automatic machine vision system provides tangible economical benefits. With such a system, manufacturing companies do not require working personnel to manually perform control of manufactured products, allowing them to concentrate on more important work. Additionally, a CV-powered system does not make mistakes, which can appear during manual control. The cost of a small human mistake can sometimes be valued at millions if not billions of dollars and Machine Vision helps to avoid it.
How
5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA
Define problem - Features model - How this model is built using skyl.ai
Not started yet, so no challenges
Data collection
Data Labeling
Data Bias
Large volumes of data
Identifying the right data set to train
Lack of knowledge of ML tools
Lack of end to end platform
Lack of expertise
Choosing the right algorithms
Monitoring the model performance
Benefit
Data is one of the most valuable resources today’s businesses have. The more information you have about your customers, the better you can understand their interests, wants and needs.
Use of relevant data sources - to get consistent and accurate data relevant for the problem you want to solve
Collection mechanisms - A formal data collection process is necessary as it ensures that the data gathered are both defined and accurate.
Small sample size - do not give the distribution of the data to the edge cases and will not train the model for exceptional cases.
Keep in mind that machine learning is a process of induction. The model can only capture what it has seen. If your training data does not include edge cases, they will very likely not be supported by the model.
The best practices can be achieved by Data Cleaning: Applying a detailed data analysis at the initial phase for recognizing which sorts of irregularities and errors must be expelled. Notwithstanding a manual assessment of the information or data samples, analytic programs are frequently expected to pick up metadata about the data resources and distinguish the issues of data quality.
Don’t let bad data or records go unresolved - remove duplicates and fill missing data;
For missing data, you should flag and fill the values.
Flag the observation with an indicator variable of missingness.
Incorrect or inconsistent data leads to false conclusions. And so, how well you clean and understand the data has a high impact on the quality of the results.
Encrypted data sources
All data sources are encrypted; thus giving users an additional layer of security, making sure your data stays safe and protected.
Access controlled flow
Defined and controlled access flows with different organizational roles like business owner, project lead, collaborators etc. allow for selective restriction so that you have full command to regulate who can view or use resources in your ML projects.
Adding tags midway - For example, the set of tags for a pizza chatbot might start with the tags “Size” “topping” and “drink” before someone realizes that you also need a “Side Dish” tag to capture Garlic Bread and Chicken Wings. Simply adding these tags and continuing work on the documents that haven’t been labeled yet poses a danger to the project. The new tags will be missing from all of the documents annotated before the new tags were added This means that your test set will be wrong for those tags, and your training data won’t contain the new tags leading to a model that won’t capture them.
<https://towardsdatascience.com/four-mistakes-you-make-when-labeling-data-7e431c4438a2>
In an annotation process, increasing the number of choices the annotator needs to make slows them down and leads to poor data quality.
Requisite Infrastructure: When launching a machine learning initiative, organizations can easily underestimate the resources they need for infrastructure. There can be substantial infrastructure requirements for machine learning, especially in the cases of image, video, and audio processing.
Cost of Infrastructure: Training and deploying a scalable infrastructure to support machine learning can be expensive and difficult to maintain.
Having a cloud approach allows experimentation with machine learning at scale without the overhead of physical hardware acquisition, configuration, and deployment.
Data & ML pipeline: AI, Machine learning and deep learning solutions require a high degree of computation speeds offered.
Model at Scale for inference: Deploying a scalable infrastructure to support machine learning can be expensive and difficult to maintain. Things get tedious and difficult to maintain at scale compared to having single server deployment or in a developer’s environment which is not a usual case.
Skills challenges: Choice of right ML algorithm - ML, DL, RL
AI product management - Dealing with Cold start, managing data labeling project, keeping transparency in the project; Keeping the model up to date.
Adoption of AI technologies requires specialists like data scientists, data engineers, infrastructure engineers and other SMEs (Subject Matter Experts).
Even with long implementation time, AI has potential to cut the expenses.
TechRepublic claims that 56% of global CEOs expect it to take 3-5 years to see any real ROI on their AI investment.
Machine automation produces quality products faster and more efficiently, while providing critical information to help managers make more informed business decisions.
Making continuous improvement part of company culture is an excellent and cost-effective approach to tackling an organization’s most difficult challenges. When supported by improvement technology, results can be achieved quickly and success can be sustained over time.
On-demand scalabilty: The truth that it’s better to have a working prototype of a smaller product, rather than an unfinished large one, still stands here with machine learning products. New ML MVPs should be prioritized based on the speed of delivery and their value to the company. If you can deliver products, even those which may be smaller, with speed, it can be a good, quick win for the whole team—you should prioritize these products first.
Organizations need to keep in mind that machine learning is an iterative process, and modifications to models might happen over time to support changing requirements.
TCPR Model: The TCPR model represents an indeterminate system—one in which more than one solution exists. In this way, it’s like a four-legged table. Engineers know that unless a four-legged table is perfectly made, and the floor on which it rests is completely flat, it’s impossible to calculate the simultaneous forces on all four legs. Moreover, the table is unbalanced and is likely resting mainly on three legs, which causes it to wobble as a result.
Link: TCPR
Thank you Mohit and Shruti, for the wonderful presentation and demo.
As mentioned earlier, the recording of the webinar will be emailed to you afterwards. [pause]
Before we get to the Q&A, I want to mention some of the offers Skyl has for those of you that are curious about incorporating Machine Learning to your business.
Skyl offers a free 1 month trial, plus Proof of Concept.
You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model!
Skyl also offers a complimentary 30 min consultation and an AI Implementation Playbook to go along.
This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges.
If you’re interested in finding out more, please visit the skyl.ai website or you can send an email directly to contact@skyl.ai.
Alright, now it’s Q&A time!
As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen.
We’ll try to answer as many questions as possible in the time that we have left.
So let’s answer some questions.
Sample questions:
Shruti
-(Julie) If I build a lot of models, how do I handle model deployment in that case?
- (Aaron) Can Skyl help me in figuring out if my data needs re-labelling?
Mohit
-(anonymous) How can Skyl help me with my data labelling needs if I have data privacy issues?
-(Jose) Apart from images, can we use Skyl for classifying text data or extracting data from documents like pdfs?
Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
All right, so we have reached the end of the webinar.
We hope you enjoyed it.
We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries,
So don’t miss out and make sure you sign up for upcoming webinars as well
Thank you for joining and I hope you have a wonderful day.