Exploring the Business Use Cases for Amazon Machine Learning - June 2017 AWS ...Amazon Web Services
Learning Objectives:
- Learn how to integrate Amazon Machine Learning with applications
- Learn how to train a model using Amazon Machine Learning - Learn how to process semi-structured log data in real-time with Amazon Machine Learning
Machine learning has been used to provide more accurate predictions than hardcoded business logic using available data. For our customers, Amazon Machine Learning is being used from helping restaurant owners, as with Upserve, to determine the right staffing level on a night; to providing more accurate cost estimates in the insurance industry, as with BuildFax. In this tech talk, we'll cover the basics of how to get started with Amazon Machine Learning, and go through an example of how to perform real-time classification of log data using Amazon Machine Learning.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure. More information: https://aws.amazon.com/machine-learning/
Complete No code solution to Machine Learning using Azure ML Studio. The aim of this presentation is to discuss the capability of Azure ML Studio in enabling any novice to perform ML experiments.
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
Exploring the Business Use Cases for Amazon Machine Learning - June 2017 AWS ...Amazon Web Services
Learning Objectives:
- Learn how to integrate Amazon Machine Learning with applications
- Learn how to train a model using Amazon Machine Learning - Learn how to process semi-structured log data in real-time with Amazon Machine Learning
Machine learning has been used to provide more accurate predictions than hardcoded business logic using available data. For our customers, Amazon Machine Learning is being used from helping restaurant owners, as with Upserve, to determine the right staffing level on a night; to providing more accurate cost estimates in the insurance industry, as with BuildFax. In this tech talk, we'll cover the basics of how to get started with Amazon Machine Learning, and go through an example of how to perform real-time classification of log data using Amazon Machine Learning.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure. More information: https://aws.amazon.com/machine-learning/
Complete No code solution to Machine Learning using Azure ML Studio. The aim of this presentation is to discuss the capability of Azure ML Studio in enabling any novice to perform ML experiments.
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
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services
Customer Guest: Pim Vernooij, Partner, Lab Digital
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to get predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Speech deliverd on 20 June 2020 at TR.AI Meetup, Istanbul
TR.AI Türkiye Yapay Zeka İnisiyatifi
AI/ML PoweredPersonalized Recommendations in Gaming Industry
Amazon Web Services - AWS
Amazon SageMaker Ground Truth: Build High-Quality and Accurate ML Training Da...Amazon Web Services
Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity using a machine learning technique called active learning. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, Ground Truth improves the ability to automatically label data resulting in high-quality training datasets.
Level: 300
Speaker: Kris Skrinak - Partner Solutions Architect, ML Global Lead, AWS
Slides from the partner event that I spoke at on the 24 April 2014. Includes and introduction to AWS and details of common adoption patterns for Enterprises that are moving to the cloud
(BDT302) Real-World Smart Applications With Amazon Machine LearningAmazon Web Services
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how an end-to-end smart application can be built in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Machine Learning: From Inception to Inference - AWS Summit SydneyAmazon Web Services
A streamlined end-to-end machine learning process enables operational efficiency and improved productivity for the data scientists, developers, and data engineers building machine learning solutions. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. In this workshop, you will be able to ingest, explore, and process the data and then build, train, and deploy a machine learning model. Further, the workshop will also guide you to connect Amazon SageMaker endpoint server to the public Internet using AWS Lambda and Amazon API Gateway in order to effectively host the model to run inferences/predictions over a Single Page Application (SPA).
This workshop requires a laptop and administrative access to your own AWS account.
Bridging the gap between Salesforce & BillingFusebill
How integrating Salesforce and your Billing System will Drive Revenue, Increase Efficiency, and Encourage Growth
The benefits of a two way integration between Salesforce and your billing system are felt far beyond the sales department.
From Finance, to Legal, to Operations, all the way up owners and CEOs, and even to your customers, this type of integration will bring positive, measurable change to your business and your bottom line.
Move Fast and (Don't) Break Things: How Big Brands Can Do Email Marketing at ...stensul
Why are so many big brands spending weeks to create their marketing emails?
Learn how to perfect your email creation process and get emails out the door faster as we look at how to:
- Eliminate bottlenecks that hurt email ROI
- Enable teams to put emails together faster
- Use content integrations to instantly make emails more engaging
- Measure the speed and efficiency of your email creation process
Amazon SageMaker - ML for every developer & data scientist ft. Workday - AIM2...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker—a fully managed service that enables developers to build, train, and deploy ML models at scale—overcomes these barriers. We review its capabilities across data labeling, model building, model training, tuning, and production hosting. Additionally, Workday—leading provider of enterprise cloud applications for financial management, human capital management, and analytics—shares how it accelerated ML throughout its organization, the benefits gained, and why it standardized on Amazon SageMaker.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services
Customer Guest: Pim Vernooij, Partner, Lab Digital
In this session from the London AWS Summit 2015 Tech Track Replay, AWS Technical Evangelist Ian Massingham introduces the new Amazon Machine Learning service.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to get predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure.
Speech deliverd on 20 June 2020 at TR.AI Meetup, Istanbul
TR.AI Türkiye Yapay Zeka İnisiyatifi
AI/ML PoweredPersonalized Recommendations in Gaming Industry
Amazon Web Services - AWS
Amazon SageMaker Ground Truth: Build High-Quality and Accurate ML Training Da...Amazon Web Services
Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity using a machine learning technique called active learning. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, Ground Truth improves the ability to automatically label data resulting in high-quality training datasets.
Level: 300
Speaker: Kris Skrinak - Partner Solutions Architect, ML Global Lead, AWS
Slides from the partner event that I spoke at on the 24 April 2014. Includes and introduction to AWS and details of common adoption patterns for Enterprises that are moving to the cloud
(BDT302) Real-World Smart Applications With Amazon Machine LearningAmazon Web Services
Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how an end-to-end smart application can be built in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Machine Learning: From Inception to Inference - AWS Summit SydneyAmazon Web Services
A streamlined end-to-end machine learning process enables operational efficiency and improved productivity for the data scientists, developers, and data engineers building machine learning solutions. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. In this workshop, you will be able to ingest, explore, and process the data and then build, train, and deploy a machine learning model. Further, the workshop will also guide you to connect Amazon SageMaker endpoint server to the public Internet using AWS Lambda and Amazon API Gateway in order to effectively host the model to run inferences/predictions over a Single Page Application (SPA).
This workshop requires a laptop and administrative access to your own AWS account.
Bridging the gap between Salesforce & BillingFusebill
How integrating Salesforce and your Billing System will Drive Revenue, Increase Efficiency, and Encourage Growth
The benefits of a two way integration between Salesforce and your billing system are felt far beyond the sales department.
From Finance, to Legal, to Operations, all the way up owners and CEOs, and even to your customers, this type of integration will bring positive, measurable change to your business and your bottom line.
Move Fast and (Don't) Break Things: How Big Brands Can Do Email Marketing at ...stensul
Why are so many big brands spending weeks to create their marketing emails?
Learn how to perfect your email creation process and get emails out the door faster as we look at how to:
- Eliminate bottlenecks that hurt email ROI
- Enable teams to put emails together faster
- Use content integrations to instantly make emails more engaging
- Measure the speed and efficiency of your email creation process
Amazon SageMaker - ML for every developer & data scientist ft. Workday - AIM2...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker—a fully managed service that enables developers to build, train, and deploy ML models at scale—overcomes these barriers. We review its capabilities across data labeling, model building, model training, tuning, and production hosting. Additionally, Workday—leading provider of enterprise cloud applications for financial management, human capital management, and analytics—shares how it accelerated ML throughout its organization, the benefits gained, and why it standardized on Amazon SageMaker.
AWS Machine Learning abstracts a lot of the complexity of a machine learning solution (e.g. cross-validation, training data set management, algorithm selection, F1-score computation) making it easy to train and deploy machine learning models.
Amazon Machine Learning: Empowering Developers to Build Smart ApplicationsAmazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning (ML) models by finding patterns in your existing data. Then, the service uses these models to process new data and generate predictions for your application. Amazon Machine Learning can ingest data from Amazon S3, Amazon Redshift or Amazon RDS. In this session, we will demonstrate how Amazon Machine Learning can be used to build an ML model, deploy it to production, and query this model from within a smart application.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
AWS re:Invent 2016: Deep Dive on Amazon Relational Database Service (DAT305)Amazon Web Services
Amazon RDS allows customers to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you up to focus on your applications and business. Amazon RDS provides you six database engines to choose from, including Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB. In this session, we take a closer look at the capabilities of RDS and all the different options available. We do a deep dive into how RDS works and the best practises to achive the optimal perfomance, flexibility, and cost saving for your databases.
AWS re:Invent 2016: Machine Learning State of the Union Mini Con (MAC206)Amazon Web Services
With the growing number of business cases for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continue to drive the development of cutting edge technology solutions. We see this manifested in computer vision, predictive modeling, natural language understanding, and recommendation engines. During this full afternoon of sessions and workshops, learn how you can develop your own applications to leverage the benefits of these services. Join this State of the Union presentation to hear more about ML and DL at AWS and see how Motorola Solutions is leveraging these state-of-the-art technologies to solve public safety challenges, and how Ohio Health intends to inject AI into the medical system.
AWS re:Invent 2016: Predicting Customer Churn with Amazon Machine Learning (M...Amazon Web Services
In this session, we take a specific business problem—predicting Telco customer churn—and explore the practical aspects of building and evaluating an Amazon Machine Learning model. We explore considerations ranging from assigning a dollar value to applying the model using the relative cost of false positive and false negative errors. We discuss all aspects of putting Amazon ML to practical use, including how to build multiple models to choose from, put models into production, and update them. We also discuss using Amazon Redshift and Amazon S3 with Amazon ML.
Amazon Machine Learning (Amazon ML) is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning models by finding patterns in your existing data. The service uses these models to process new data and generate predictions for your application. In this session, we will show you how to use machine learning with the data you already have to arrive at accurate and actionable predictions - to create smart applications. You will learn how to use and integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.
Integrating Amazon SageMaker into your Enterprise - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Get an introduction to Amazon SageMaker
- Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment
- View a walkthrough of the machine learning lifecycle to cover best practices in the ML process
At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years. Machine learning (ML) algorithms drive many of our internal systems. It's also core to the capabilities our customers experience – from the path optimization in our fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience Amazon Go. This is just the beginning. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Amazon의 머신러닝 솔루션: Fraud Detection & Predictive Maintenance - 남궁영환 (AWS 데이터 사이...Amazon Web Services Korea
인공지능, 머신 러닝은 비즈니스의 필수 기술이 되고 있습니다. 하지만 여전히 기술을 손쉽게 도입하기엔 어려움이 있습니다. 본 세션에서는 클라우드 상에서 머신러닝 기반 애플리케이션을 손쉽게 구현할 수 있는 AWS의 솔루션들에 대해 살펴봅니다.
다시보기 링크: https://youtu.be/UHvBYgCZiI4
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
Financial services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, hear how IHS Markit has used machine learning on AWS to help global banking institutions manage their commodities portfolios. Learn how Amazon Machine Learning can take the hassle out of AI.
by Yash Pant, Enterprise Solutions Architect AWS
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walk through the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Building WhereML, an AI Powered Twitter Bot for Guessing Locations of Picture...Amazon Web Services
The WhereML Twitter bot is built on the LocationNet model which is trained with the Berkley Multimedia Commons public dataset of 33.9 million geotagged images from Flickr (and other sources). The model is based on a ResNet-101 architecture and adds a classification layer that splits the earth into ~15000 cells created with Google’s S2 spherical geometry library. This model is based on prior work completed at Berkley and Google.
In this session we’ll start by describing AI in general terms then diving into deep learning and the MXNet framework. We’ll describe the LocationNet model in detail and show how it is trained and created in Amazon SageMaker. Finally, we’ll talk about the Twitter Account Activity webhooks API and how to interact with it using an API Gateway and AWS Lambda function.
Attendees are encouraged to interact with the bot in real-time at whereml.bot or on twitter at @WhereML
All code used in this project is open source and was written live on twitch.tv/aws and attendees are encouraged to experiment with it.
Building prediction models with Amazon Redshift and Amazon Machine Learning -...Amazon Web Services
Mining data with Redshift, using this data to build a prediction model with Amazon ML, performing batch predictions & real-time predictions (with a Java app).
WhereML a Serverless ML Powered Location Guessing Twitter BotRandall Hunt
Learn how we designed, built, and deployed the @WhereML Twitter bot that can identify where in the world a picture was taken using only the pixels in the image. We'll dive deep on artificial intelligence and deep learning with the MXNet framework and also talk about working with the Twitter Account Activity API. The bot is entirely autoscaling and powered by Amazon API Gateway and AWS Lambda which means, as a customer, you don't manage any infrastructure. Finally we'll close with a discussion around custom authorizers in API Gateway and when to use them.
An introduction to computer vision with Hugging FaceJulien SIMON
In this code-level talk, Julien will show you how to quickly build and deploy computer vision applications based on Transformer models. Along the way, you'll learn about the portfolio of open source and commercial Hugging Face solutions, and how they can help you deliver high-quality solutions faster than ever before.
Starting your AI/ML project right (May 2020)Julien SIMON
In this talk, we’ll see how you can put your AI/ML project on the right track from the get-go. Applying common sense and proven best practices, we’ll discuss skills, tools, methods, and more. We’ll also look at several real-life projects built by AWS customers in different industries and startups.
Building Machine Learning Inference Pipelines at Scale (July 2019)Julien SIMON
Talk at OSCON, Portland, 18/07/2019
Real-life Machine Learning applications require more than a single model. Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. Predictions may need post-processing: filtering, sorting, combining, etc.
Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker)
Optimize your Machine Learning Workloads on AWS (July 2019)Julien SIMON
Talk at Floor 28, Tel Aviv.
Infrastructure, tips to speed up training, hyperparameter optimization, model compilation, Amazon SageMaker Neo, cost optimization, Amazon Elastic Inference
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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.
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.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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/
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
5. Jeff Immelt, GE Chairman & CEO
“If you went to bed last night as an industrial company, you’re going
to wake up this morning as a software and analytics company.”
7. Machine Learning @ Amazon.com
Amazon builds and uses Machine Learning solutions in hundreds of
services across its various businesses. No off the shelf solution could
handle the scale.
Product recommendation is visible in many services.
8. Smart self-service
Amazon also uses Machine Learning for Customer
Support, building models based on recent orders, click-
stream, user devices, prime membership usage, recent
cases, recent account changes, etc.
The models are used to provide efficient self-service to
our customers.
The self-service page is generated according to what
your most likely question will be.
9.
10. Smart customer call routing
Not all customer interactions can be solved with self-service.
Therefore, Amazon operates large support centers where
Customer Service Representatives (CSR) handle customer
requests.
The Machine Learning models described above are used to
optimize the human interactions of these requests. For
example, they are used to route the customer call to the best
CSR before the customer has even started to speak!
16. Collect Store Analyze Consume
A
iOS
Android
Web Apps
Logstash
Amazon
RDS
Amazon
DynamoDB
Amazon
ES
Amazon
S3
Apache
Kafka
Amazon
Glacier
Amazon
Kinesis
Amazon
DynamoDB
Amazon
Redshift
Impala
Pig
Amazon ML
Streaming
Amazon
Kinesis
AWS
Lambda
AmazonElasticMapReduce
Amazon
ElastiCache
SearchSQLNoSQLCache
StreamProcessing
Batch
Interactive
Logging
StreamStorage
IoT
Applications
FileStorage
Analysis&Visualization
Hot
Cold
Warm
Hot
Slow
Hot
ML
Fast
Fast
Amazon
QuickSight
Transactional Data
File Data
Stream Data
Notebooks
Predictions
Apps & APIs
Mobile
Apps
IDE
Search Data
ETL
17. Amazon Machine Learning
Easy-to-use, managed machine learning service
built for developers
Robust, powerful machine learning technology
based on Amazon’s internal systems
Create models using your data already stored in
the AWS Cloud
Deploy models to production in seconds
18. BuildFax
“Amazon Machine Learning
democratizes the process
of building predictive
models. It's easy and fast to
use, and has machine-
learning best practices
encapsulated in the
product, which lets us
deliver results significantly
faster than in the past”
Joe Emison, Founder &
Chief Technology Officer
https://aws.amazon.com/solutions/case-studies/buildfax/
19. Upserve
Upserve is a software and mobile point of sale provider that offers a cloud-based
restaurant management platform to restaurant owners across the U.S.
“Using Amazon Machine Learning, we can predict the total number of customers
who will walk through a restaurant’s doors in a night. As a result, restaurateurs
can better prep and plan their staffing for that night”
“It only took two weeks from the time we decided to use the technology to the
moment we started using predictive data in the daily email we send out. And we
immediately saw Amazon ML beating the baseline to predicting nightly covers”
Bright Fulton, Director of Infrastructure Engineering
https://aws.amazon.com/solutions/case-studies/upserve/
22. Machine Learning for business
Can I spot the right prospects ?
What drives my upsell for this product ?
Can I predict contract cancellations ?
23. Standard way
StructureDataset
Model/
Algorithm
Preprocessing
Model and
validation
Deployment
Predictions
-Feature engineering
-Feature selection
-Feature transformation
-Missing value
-Outlier handling
-Variable type
-Validate partioning
-Choose model metric
-Score models
-Guess which algo to run
-Model selection
-Tuning hyper-parameters
-Implementation in the application
-Recoding in another language in production
-Testing
Other
sources
Importdata
30. Resources
Big Data Whitepaper: http://bit.ly/2deGEVL
Case studies: https://aws.amazon.com/solutions/case-studies/big-data/
Big Data Architectural Patterns and Best Practices on AWS
https://www.youtube.com/watch?v=K7o5OlRLtvU
Real-World Smart Applications With Amazon Machine Learning
https://www.youtube.com/watch?v=sHJx1KJf8p0
Deep Learning: Going Beyond Machine Learning
https://www.youtube.com/watch?v=Ra6m70d3t0o