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In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.
Slide deck of my DyanmicsPower! Brussels session.
Session description:
AI Builder is here!
The Citizen Developer style tool to easily add Artificial Intelligence to your PowerApps and Flows.
In this session we will take a deep dive into this great new tool in the Power Platform stack. How to build your own models and how to embed them in your apps and flows.
We will look at the what is possible, but also what are the limits and boundaries you need to take in consideration.
We will cover best practices and tips and tricks.
In a nutshell: everything you need to know to get started yourself.
Machine Learning: Artificial Intelligence isn't just a Science Fiction topicRaúl Garreta
In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.
Slide deck of my DyanmicsPower! Brussels session.
Session description:
AI Builder is here!
The Citizen Developer style tool to easily add Artificial Intelligence to your PowerApps and Flows.
In this session we will take a deep dive into this great new tool in the Power Platform stack. How to build your own models and how to embed them in your apps and flows.
We will look at the what is possible, but also what are the limits and boundaries you need to take in consideration.
We will cover best practices and tips and tricks.
In a nutshell: everything you need to know to get started yourself.
There are many ways of getting the Sentiment Analytics done, however, using a Cloud Agnostic approach could lead to faster deployment and better analytics at a lower cost.
https://www.becloudready.com/
Build, Train, and Deploy Machine Learning for the Enterprise with Amazon Sage...Amazon Web Services
Machine learning (ML) is rapidly being adopted by enterprises, enabling them to be nimble and align technical solutions to solve real-world business problems. ML use cases include diagnosis and research in healthcare, financial fraud detection, natural language processing (NLU), and accurate statistics in sports. Amazon SageMaker is a fully managed platform that enables developers to build, train, and deploy enterprise-scale ML models quickly and easily. In this workshop, we build an ML model using Amazon SageMaker’s built-in algorithms and frameworks. We train the model to achieve a high level of accurate predictions, then we deploy the model in production to achieve best results. Gain an understanding of how Amazon SageMaker removes the complexity and barriers to use and deploy ML models.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
As machine learning has is permeating more and more industries and businesses, the need for audit professionals to provide assurance over machine learning is growing. Andrew's presentation will provide an audit-centric overview of machine learning and present a framework for how to begin auditing machine learning in your organization.
Lessons learnt and system built while solving the last mile problem in machine learning - taking models to production. Used for the talk at - http://sched.co/BLvf
MOPs & ML Pipelines on GCP - Session 6, RGDCgdgsurrey
MLOps Lifecycle
ML problem framing
ML solution architecture
Data preparation and processing
ML model development
ML pipeline automation and orchestration
ML solution monitoring, optimization, and maintenance
Learn what is happening in Silicon Valley regarding advances in Artificial Intelligence through this presentation. We shared this presentation with the AI expo visitors.
When We Spark and When We Don’t: Developing Data and ML PipelinesStitch Fix Algorithms
The data platform at Stitch Fix runs thousands of jobs a day to feed data products that provide algorithmic capabilities to power nearly all aspects of the business, from merchandising to operations to styling recommendations. Many of these jobs are distributed across Spark clusters, while many others are scheduled as isolated single-node tasks in containers running Python, R, or Scala. Pipelines are often comprised of a mix of task types and containers.
This talk will cover thoughts and guidelines on how we develop, schedule, and maintain these pipelines at Stitch Fix. We’ll discuss guidelines on how we think about which portions of the pipelines we develop to run on what platforms (e.g. what is important to run distributed across Spark clusters vs run in stand-alone containers) and how we get them to play well together. We’ll also provide an overview of tools and abstractions that have been developed at Stitch Fix to facilitate the process from development, to deployment, to monitoring them in production.
Predictive Analytics Project in Automotive IndustryMatouš Havlena
Original article: http://www.havlena.net/en/business-analytics-intelligence/predictive-analytics-project-in-automotive-industry/
I had a chance to work on a predictive analytics project for a US car manufacturer. The goal of the project was to evaluate the feasibility to use Big Data analysis solutions for manufacturing to solve different operational needs. The objective was to determine a business case and identify a technical solution (vendor). Our task was to analyze production history data and predict car inspection failures from the production line. We obtained historical data on defects on the car, how the car moved along the assembly line and car specific information like engine type, model, color, transmission type, and so on. The data covered the whole manufacturing history for one year. We used IBM BigInsights and SPSS Modeler to make the predictions.
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Ali Alkan
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi | Automating Machine Learning, Artificial Intelligence, and Data Science | Guided Analytics
Top 5 Travel Analytics Solutions Companies.pptxKavika Roy
Data analytics has a crucial role in the ever-changing travel industry. It helps businesses stay at the top of the game while increasing ROI. Here, we’ll discuss the top travel analytics solutions provider to partner with in the US market.
Transforming Hotel Data Analytics with a Resilient Datawarehouse.pptxKavika Roy
The travel and hospitality industry is evolving through the adoption of data analytics and BI solutions. This is done by modernizing the hotel data analytics infrastructure. Here, we’ll discuss the ways to build a resilient data warehouse and the role of analytics in the industry.
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There are many ways of getting the Sentiment Analytics done, however, using a Cloud Agnostic approach could lead to faster deployment and better analytics at a lower cost.
https://www.becloudready.com/
Build, Train, and Deploy Machine Learning for the Enterprise with Amazon Sage...Amazon Web Services
Machine learning (ML) is rapidly being adopted by enterprises, enabling them to be nimble and align technical solutions to solve real-world business problems. ML use cases include diagnosis and research in healthcare, financial fraud detection, natural language processing (NLU), and accurate statistics in sports. Amazon SageMaker is a fully managed platform that enables developers to build, train, and deploy enterprise-scale ML models quickly and easily. In this workshop, we build an ML model using Amazon SageMaker’s built-in algorithms and frameworks. We train the model to achieve a high level of accurate predictions, then we deploy the model in production to achieve best results. Gain an understanding of how Amazon SageMaker removes the complexity and barriers to use and deploy ML models.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
As machine learning has is permeating more and more industries and businesses, the need for audit professionals to provide assurance over machine learning is growing. Andrew's presentation will provide an audit-centric overview of machine learning and present a framework for how to begin auditing machine learning in your organization.
Lessons learnt and system built while solving the last mile problem in machine learning - taking models to production. Used for the talk at - http://sched.co/BLvf
MOPs & ML Pipelines on GCP - Session 6, RGDCgdgsurrey
MLOps Lifecycle
ML problem framing
ML solution architecture
Data preparation and processing
ML model development
ML pipeline automation and orchestration
ML solution monitoring, optimization, and maintenance
Learn what is happening in Silicon Valley regarding advances in Artificial Intelligence through this presentation. We shared this presentation with the AI expo visitors.
When We Spark and When We Don’t: Developing Data and ML PipelinesStitch Fix Algorithms
The data platform at Stitch Fix runs thousands of jobs a day to feed data products that provide algorithmic capabilities to power nearly all aspects of the business, from merchandising to operations to styling recommendations. Many of these jobs are distributed across Spark clusters, while many others are scheduled as isolated single-node tasks in containers running Python, R, or Scala. Pipelines are often comprised of a mix of task types and containers.
This talk will cover thoughts and guidelines on how we develop, schedule, and maintain these pipelines at Stitch Fix. We’ll discuss guidelines on how we think about which portions of the pipelines we develop to run on what platforms (e.g. what is important to run distributed across Spark clusters vs run in stand-alone containers) and how we get them to play well together. We’ll also provide an overview of tools and abstractions that have been developed at Stitch Fix to facilitate the process from development, to deployment, to monitoring them in production.
Predictive Analytics Project in Automotive IndustryMatouš Havlena
Original article: http://www.havlena.net/en/business-analytics-intelligence/predictive-analytics-project-in-automotive-industry/
I had a chance to work on a predictive analytics project for a US car manufacturer. The goal of the project was to evaluate the feasibility to use Big Data analysis solutions for manufacturing to solve different operational needs. The objective was to determine a business case and identify a technical solution (vendor). Our task was to analyze production history data and predict car inspection failures from the production line. We obtained historical data on defects on the car, how the car moved along the assembly line and car specific information like engine type, model, color, transmission type, and so on. The data covered the whole manufacturing history for one year. We used IBM BigInsights and SPSS Modeler to make the predictions.
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi...Ali Alkan
Makine Öğrenmesi, Yapay Zeka ve Veri Bilimi Süreçlerinin Otomatikleştirilmesi | Automating Machine Learning, Artificial Intelligence, and Data Science | Guided Analytics
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💥 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
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Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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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/
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All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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2. Main Components of AI
● Learning Procedures:
Collecting and processing data for meaningful insights.
● Reasoning Procedures:
Selecting the most suitable algorithms for specific tasks.
● Self-adjustment Procedures:
Monitoring and adjusting algorithms for enhanced performance.
3. HHow to begin building AI Products?
w to begin building AI Products?
Identify the Problem
● Focus on pain points and value proposition.
● Define the problem to be solved.
Review Your Data
● Select relevant data sources.
● Data cleaning and processing for model training.
4. HHow to begin building AI Products?
w to begin building AI Products?
Select a Viable Platform
Cloud Frameworks
● Advantages and considerations.
● Examples: AWS, Google Cloud AI, IBM Cloud, Microsoft Azure AI.
In-house Frameworks
● Advantages and considerations.
● Examples: TensorFlow, Keras, Microsoft Cognitive Toolkit, Tableau.
5. HHow to begin building AI Products?
w to begin building AI Products?
Select a Programming Language
● Overview of programming languages (R, Python, Java, C++).
● Considerations for choosing a language based on needs.
Create Algorithms
● Train the algorithm with collected data.
● Optimize for high accuracy.
6. HHow to begin building AI Products?
w to begin building AI Products?
Implement, Monitor, and Optimize
● Feasibility check.
● Performance assessment of deployed models and algorithms.
● Key considerations for optimization.
7. HChallenges of Building AI Products
w to begin building AI Products?
Data Quality and Quantity
● Impact on AI algorithm effectiveness.
● Ensuring sufficient and accurate information.
Ethical Considerations
● Avoiding biases and discrimination.
● Advocating ethical and legal standards.
8. Challenges of Building AI Products
w to begin building AI Products?
Technical Expertise
● Workforce training or external hiring.
● Addressing the need for technical skills.
Security Concerns
● Potential for theft or misuse of information.
● Implementing security techniques.
9. w to begin building AI Products?
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