Microsoft provides an AI platform and tools for developers to build, train, and deploy intelligent applications and services. Key elements of Microsoft's AI offerings include:
- A unified AI platform spanning infrastructure, tools, and services to make AI accessible and useful for every developer.
- Powerful tools for AI development including deep learning frameworks, coding and management tools, and AI services for tasks like computer vision, natural language processing, and more.
- Capabilities for training models at scale using GPU accelerated compute on Azure and deploying trained models as web APIs, mobile apps, or other applications.
- A focus on trusted, responsible, and inclusive AI that puts users in control and augments rather than replaces human
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
I am an instructor of the MLOps workshop for some anonymous startup incubation program where the objectives are (1) to orchestrate and deploy updates to the application and the deep learning model in a unified way. (2) To design a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the GPT-3, GPT-4, DALL-E, Codex, and Embeddings model series. These models can be easily adapted to any specific task, including but not limited to content generation, summarization, semantic search, translation, transformation, and code generation. Microsoft offers the accessibility of the service through REST APIs, Python or C# SDK, or the Azure OpenAI Studio.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
I am an instructor of the MLOps workshop for some anonymous startup incubation program where the objectives are (1) to orchestrate and deploy updates to the application and the deep learning model in a unified way. (2) To design a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including the GPT-3, GPT-4, DALL-E, Codex, and Embeddings model series. These models can be easily adapted to any specific task, including but not limited to content generation, summarization, semantic search, translation, transformation, and code generation. Microsoft offers the accessibility of the service through REST APIs, Python or C# SDK, or the Azure OpenAI Studio.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
2018 11 14 Artificial Intelligence and Machine Learning in AzureBruno Capuano
Slides used during my session "Artificial Intelligence and Machine Learning in Azure" for The Azure Group (Canada's Azure User Community) on November 14 2018.
Public group
Tour de France Azure PaaS 6/7 Ajouter de l'intelligenceAlex Danvy
Nous assisterons probablement à une rupture générationnelle entre les apps avec de l'intelligence artificielle et celles sans. Ces dernières, comme les applications en mode caractères à l'arrivée des interfaces graphiques, auront du mal à perdurer.
Azure met à dispositions 3 approches pour ajouter de l'IA dans une app, avec un niveau de difficulté graduel, de l'outil ne nécessitant aucune compétence particulière à celui dédié aux Data Scientistes.
Microsoft is working hard to make Artificial Intelligence available to everyone. We not only infuse AI in our products but also give you the platform to build your very own solution, that you are a developer, a citizen data scientist or a hard core data scientist.
Join Joseph Sirosh, Corporate Vice President of the Cloud AI Platform, for a deep dive into the AI platform and exciting AI use cases. Joseph will showcase how every developer can infuse intelligence into their applications and create amazing new experiences with AI. In this exciting overview, you will learn about the application of AI technologies in the cloud. We will help you understand how to add pre-built AI capabilities like object detection, face understanding, translation and speech to applications. We will show how developers can build Cognitive Search applications that understand deep content in images, text and other data. We will also show how the platform can be used to build your own custom AI models for predictive applications and how to use the Azure platform to accelerate machine learning. Joseph will also show how companies assemble end-to-end systems of intelligence using the rich variety of data and application development services on Azure.
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
Microsoft & Machine Learning / Artificial Intelligenceİbrahim KIVANÇ
In this presentation you'll find Machine Learning / Deep Learning tools and services from Microsoft. Including Azure Machine Learning Workbench, Azure Notebooks, Azure Data Science Virtual Machines and more.
Here are the demos & resources
https://github.com/ikivanc/Azure-ML-Workbench-Iris-Dataset-Classification
https://github.com/ikivanc/Azure-ML-Resources
Neuron is a server-less Deep Learning and AI experiment platform for analytics where you can build, deploy and visualise the data models.
Practical lab on cloud access from anywhere.
by Mahendra Bairagi, AI Specialist Solutions Architect, AWS
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun. You are looking to use Machine Learning and IoT technologies to solve this unique problem.
Do you accept the Challenge?
The objective of this task is to help the festival-goers quickly identify the DJ stage where crowd is the happiest. You've seen a lot of buzz around computer vision, machine learning, and IoT and want to use this technology to detect crowd emotions. From your initial research there are existing ML models that you can leverage to do face and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches and find out!
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
In this opportunity I spoke for almost 4 hours -with a lunch in between- about the analytics solutions on azure and it's tool for machine learning and cognitive services. I introduced the automated machine learning on Azure with some demos in real time.
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun.
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
3. Amplifying human ingenuity
Trusted and flexible approach
that puts you in control
Powerful platform that makes
AI accessible
that extend your
capabilities
Innovate and accelerate with
powerful tools and services that
bring AI to every developer.
Drive your digital
transformation with
accelerators, solutions, and
practices to empower your
organization with AI.
Experience the intelligence built
into Microsoft products and
services you use every day.
Cortana is helping you stay on
top of it all so you can focus on
what matters most.
microsoft.com/ai
4. AI platform
VS Tools
for AI
Azure ML
Studio
CODING & MANAGEMENT TOOLS
Azure ML
Workbench
DEEP LEARNING FRAMEWORKS
Cognitive
Toolkit
TensorFlow Caffe
Others (Scikit-learn, MXNet, Keras,
Chainer, Gluon…)
3rd Party
Others (PyCharm, Jupyter Notebooks…)
AI ON DATA
Cosm
os DB
AI COMPUTE
SQL
DB
SQL
DW
Data
Lake
Spark
DS
VM
Batch
AI
ACS
CPU, FPGA, GPU
Edge
CUSTOM SERVICESCONVERSATIONAL AI TRAINED SERVICES
Azure Bot Service Azure Machine LearningCognitive Services
Services
Infrastructure
Tools
azure.microsoft.com/ai
5. How can I start?
AI-as-a-Service
Leverage AI APIs
Data + AI
Add AI where the data is
AI
Create & train models
6. SQL Server 2017 Machine Learning Services
In-database Python & R integration
Run Python & R in stored procedures
Remote compute contexts for Python & R
Gain access to libraries from open source ecosystem
Built-in Machine Learning Algorithms
MicrosoftML package includes customizable deep neural
networks, fast decision trees and decision forests, linear
regression, and logistic regression
Access to pre-trained models such as image recognition
Real-time and native scoring
Model stored in optimized binary format, enabling faster
scoring operations without calling R runtime
Native T-SQL function for fast scoring
7. Azure Data Lake
Hyper-scale data store
optimized for analytics
• Petabyte size files and
Trillions of objects
• Scalable throughput for
massively parallel analytics
• HDFS for the Cloud
• Always encrypted, Role-
based Security & Auditing
• Enterprise-grade Support
Big data queries as a
service
• Start in seconds, Scale
instantly, Pay per job
• Develop massively parallel
programs with simplicity
• Debug and Optimize your
Big Data programs with ease
• Virtualize your analytics
• Enterprise-grade Support
and Security
Store Analytics
Cognitive capabilities in
big data programs
• Face API
• Image Tagging
• Emotion Analysis
• OCR
• Text Key Phrase extraction
• Text sentiment analysis
Ingest all data
regardless of
requirements
Store all data
in native format
without schema
definition
Do analysis
Using analytic
engines like
Hadoop and ADLA
8. Microsoft Cognitive Services
Vision
Computer Vision
Custom Vision Service
Content Moderator
Emotion API
Face API
Video Indexer
Speech
Bing Speech Service
Custom Speech Service
Speaker Recognition
Translator Speech
Language
Bing Spell Check
Language
Understanding
Intelligent Service (LUIS)
Linguistics Analysis
Text Analytics
Translator Text
Web Language Model
Knowledge
Custom Decision Service
QnA Maker
Knowledge Exploration
Entity Linking
Academic Knowledge
Search
Bing Web Search
Bing Custom Search
Bing Autosuggest
Bing News Search
Bing Video Search
Bing Entity Search
Bing Image Search
10. Microsoft Cognitive Toolkit
Differentiates automatically and trains the net when
users implement the forward direction of the network
Unified framework supporting a wide range of uses
• FNN, RNN, LSTM, CNN, DSSM, GAN, etc.
• All types of deep learning applications: e.g., speech, vision
and text
C++, C#, Java, Python; Linux and Windows
Distributed training
• Can scale to hundreds of GPUs and VM’s
Open source
• Hosted on GitHub – Jan 25, 2016
• Contributors from Microsoft and external (MIT, Stanford,
etc.) Input layer Hidden layer 1 Hidden layer 2 Output layer
A A
x
x x
+
tanh
tanhơ ơ ơ
11. Azure Machine Learning
Spark
SQL Server
Virtual machines
GPUs
Container services
Notebooks
IDEs
Azure Machine Learning
Workbench
SQL Server
Machine Learning
Server
ON-
PREMISES
EDGE
COMPUTING
Azure IoT Edge
Experimentation
and Model
Management
AZURE MACHINE
LEARNING SERVICES
TRAIN & DEPLOY
OPTIONS
A ZURE
Built with open source tools
Jupyter Notebook, Apache Spark, Docker,
Kubernetes, Python, Conda
Studio
Workbench
Experimentation Service
Model Management Service
Libraries for Apache Spark
(MMLSpark Library)
Visual Studio Code Tools for AI
12. Deep Learning in Azure
Create model using Azure Data Science Virtual Machine
with GPU
Ubuntu 16.04 LTS, OpenLogic 7.2 CentOS, Windows Server 2012
CNTK, Tensorflow, MXNet, Caffe & Caffe2, Torch, Theano, Keras, Nvidia
Digits, etc.
Train and score models using Azure Batch AI Training
with Dockerized tools
Provision multi-node CPU/GPU and VM set jobs
Execute massively parallel computational workflows
Hardware microservices using FPGA
Deploy trained models as web API’s
Multiple compute technologies – Virtual Machines, Container Service,
Service Fabric, App Service, Edge, etc.
13. Data Science Virtual Machine
Data Science Tools
Anaconda Python 2.7 and 3.5, JupyterHub
Microsoft R Server 9.1 with R Open 3.3.3
Spark local 2.1.1 with PySpark & SparkR Jupyter
kernels
Single node local Hadoop (HDFS, Yarn)
Visual Studio Code, IntelliJ IDEA, PyCharm, & Atom
Apache Drill, JuliaPro, Vowpal Wabbit, xgboost, etc.
Deep Learning Tools
CNTK, TensorFlow, MXNet, Caffe, Caffe2, DIGITS,
H2O, Keras, Theano, Torch
GPU and CPU
NVIDIA driver, CUDA, cuDNN
14. DNN Processing Units
EFFICIENCY
Silicon alternatives for DNNs
14
FLEXIBILITY
Soft DPU
(FPGA)
Contro
l Unit
(CU)
Registers
Arithmeti
c Logic
Unit
(ALU)
CPUs GPUs
ASICsHard
DPU
Cerebras
Google TPU
Graphcore
Groq
Intel Nervana
Movidius
Wave Computing
Etc.
BrainWave
Baidu SDA
Deephi Tech
ESE
Teradeep
Etc.
15. FPGA
F F F
L0
L1
F F F
L0
Pretrained DNN Model
in CNTK, etc.
Scalable DNN Hardware
Microservice
BrainWave
Soft DPU
Instr Decoder
& Control
Neural FU
Network switches
FPGAs
18. Data & Analytics Platform
Model & servePrep & train
Data Lake
Analytics
D A T A
Business apps
Custom apps
Sensors and devices
I N T E L L I G E N C E A C T I O N
Store
Data Lake
Store
Ingest
Data Factory
Machine Learning
Web & mobile appsCosmos DB
SQL DB
Analytical dashboards
SQL Data
Warehouse
Analysis
Services
Operational reports
HDInsight
(Hadoop/Spark)
Stream Analytics
Event Hubs
Kafka on HDInsight
Blobs
27. Deep Learning
Traditional machine learning requires manual feature extraction /
engineering
Deep learning can automatically learn features in data
Feature extraction for unstructured data is difficult
Common DNNs
• DCNN (deep convolutional neural network) – to extract
representation from images
• RNN (recurrent neural network) – to extract representation from
sequential data
• LSTM (long short-term memory) – popular in natural language
processing
• DBN (deep belief neural network) – to extract hierarchical
representation from a dataset
28. Deep Learning
Labrador
Larger and deeper networks
Many layers; some up to 150 layers
Billions of learnable parameters
Feed Forward, Recurrent, Convolutional,
Sparse, etc.
Trained on big data sets
10,000+ hours of speech
Millions of images
Years of click data
Highly parallelized computation
Long-running training jobs (days, weeks, months)
Acceleration with GPU
Recent advances in more computer power and
big data
29. Designing a solution for deep learning
TestingPreparation Development Training Operationalize
• Evaluate the model on
separate data sets
(ground truth)
• Data access
• Data preparation
• Labeled data set
• Data management
• Storage performance
• Network performance
• Re-training
automation
• Data reading
• Data pre-processing
• Model creation (e.g.
layer architecture)
• Learning & evaluation
• Model optimization
(e.g., parameter
tuning, SGD, batch
sizes,
backpropagation,
convergence &
regularization
strategies, etc.)
• High-scale job
scheduling
• On-demand compute
infrastructure
• Managed task
execution
• Data / model
parallelism
• Data transfer
• Compute
infrastructure
• Deploy and serve the
model
• Model dependencies
• Feedback loop
• Application
architecture
• DevOps toolchain
A subset of tasks in Microsoft Team Data Science Process Lifecycle (TDSP)
30. Model Development
Used in Microsoft first-party AI implementations
Unified framework supporting a wide range of uses
FNN, RNN, LSTM, CNN, DSSM, etc.
All types of deep learning applications: e.g., speech, vision and
text
C++, C#, Java, Python; Linux and Windows
Distributed training
Can scale to hundreds of GPUs and VM’s
Open source
Hosted on GitHub – Jan 25, 2016
Contributors from Microsoft and external (MIT, Stanford, etc.)
15K 15K 15K 15K 15K
500 500 500
max max
...
...
... max
500
...
...
Word hashing layer: ft
Convolutional layer: ht
Max pooling layer: v
Semantic layer: y
<s> w1 w2 wT <s>Word sequence: xt
Word hashing matrix: Wf
Convolution matrix: Wc
Max pooling operation
Semantic projection matrix: Ws
... ...
500
31. Model Development
Data set of hand written digits with
60,000 training images
10,000 test images
Each image is: 28 x 28 pixels
Vector (array) of 784 elements
Labels encoded using 1-hot encoding
(e.g., 5 = “labels 0 0 0 0 0 1 0 0 0 0”)
Apply data transformations
Shuffle training data
Add noise (e.g., numpy.random)
Distort images with affline transformation
(translations or rotations)
1 5 4 3
5 3 5 3
5 9 0 6
Corresponding labelsHandwritten images
32. Model Development
S S
0.1 0.1 0.3 0.9 0.4 0.2 0.1 0.1 0.6 0.3
Model
SBias (10)
(𝑏)
0 1 9
…
784 pixels ( 𝑥)
28 pix
28pix
S = Sum (weights x pixels) = 𝑤0 ∙ 𝑥 𝑇
784 784
General solution approach
• A corresponding weight array for each element in the input
array
• Find the suitable weights to classify the image vector into
corresponding digit
• Repeat the process 10 times; each for the digits from 0-9
• Compute the output of the classifiers (10 of them) by
multiplying all the weights with the corresponding pixels
• Add a scalar value called bias to each of the summation
units
• Normalize output of summation units to a 0-1 range using a
sigmoid activation function
33. Model Development
softmax
import cntk as C
input_dim = 784
num_output_classes = 10
input = C.input_variable(input_dim)
label = C.input_variable(num_output_classes)
def create_model(features):
with C.layers.default_options(init = C.glorot_uniform()):
r = C.layers.Dense(num_output_classes, activation = None)(features)
return r
34. Model Development
num_hidden_layers = 2
hidden_layers_dim = 400
def create_model(features):
with C.layers.default_options(init = C.layers.glorot_uniform(),
activation = C.ops.relu):
h = features
for _ in range(num_hidden_layers):
h = C.layers.Dense(hidden_layers_dim)(h)
r = C.layers.Dense(num_output_classes, activation = None)(h)
return r
softmax
35. Model Development
def create_model(features):
with C.layers.default_options(init=C.glorot_uniform(), activation=C.relu):
h = features
h = C.layers.Convolution2D(filter_shape=(5,5), num_filters=8, strides=(2,2),
pad=True, name='first_conv')(h)
h = C.layers.Convolution2D(filter_shape=(5,5), num_filters=16, strides=(2,2),
pad=True, name='second_conv')(h)
r = C.layers.Dense(num_output_classes, activation=None, name='classify')(h)
return r
36. Model Development
Initialization
Data loading and reading
Network setup
Loss function
Error function
Learning algorithms (SGD, AdaGrad, etc.)
Minibatch sizing
Learning rate
Training
Evaluation / testing
37. Training
1. Create a DNN training script with any DL framework
2. Package the DNN as a Docker image and upload it
to the Azure Container Registry
3. Create a pool with GPU VMs
4. Add a job with tasks to run a hyper-parameter
sweep experiment tasks
5. Tasks are scheduled to the pool and the Docker
image is downloaded if required
6. Data is copied to the container
7. Tasks as containers perform the DNN training
8. Tasks write results and trained models to storage
DSVM
38. Operationalization
Batch scoring
Azure Batch AI Training
Azure HDInsight on Spark
SQL Server 2017 (GPU-host with DL libraries, DNN scoring module
in Python, execute registered stored procs)
Real-time scoring
Azure Machine Learning Operationalization (CLI)
Azure Container Service (Docker Swarm, DC/OS, Kubernetes)
Azure App Service Web Apps (Windows, Linux)
edge node
Azure
Data Lake
Storage
Azure
HDInsight
39. Operationalization
Sample workflow:
1. Create a driver file for a trained DNN; use requirements.txt for pip
configuration and dependencies
2. Setup of the cluster from AML CLI (Azure Machine Learning Command
Line Interface)
3. Create a web-service, image uploaded to Docker Registry (files
packaged as a nginx/flask web-service in a Docker image and stored in a
private Azure Docker Registry)
4. Deploys web-service locally
5. Test locally
6. Deploy to cluster (monitoring and management using Marathon UI)
7. Send requests to web-service
DSVM
https://github.com/Azure/Machine-Learning-Operationalization/
40. Operationalization
Sample application workflow:
1. Develop and test locally a flask
web-service that load the model
in memory and handles requests
2. Create a deployment script to
set-up the dependencies on the
Azure App Service environment
3. Git commit and push to repo
4. Deployment triggered on Azure
Web App instance configured
with Github continuous
deployment
5. Send requests to the Web App
DSVM DSVM
Sample container workflow:
1. Develop and test locally a flask-
based web-service container
that loads the model in
memory and handles requests
2. Build and upload the Docker
image to a registry
3. Trigger deployment of Azure
Web App
4. Send requests to the Web App
41. Designing a solution for deep learning
TestingPreparation Development Training Operationalize
• Evaluate the model on
separate data sets
(ground truth)
• Data access
• Data preparation
• Labeled data set
• Data management
• Storage performance
• Network performance
• Re-training
automation
• Data reading
• Data pre-processing
• Model creation (e.g.
layer architecture)
• Learning & evaluation
• Model optimization
(e.g., parameter
tuning, SGD, batch
sizes,
backpropagation,
convergence &
regularization
strategies, etc.)
• High-scale job
scheduling
• On-demand compute
infrastructure
• Managed task
execution
• Data / model
parallelism
• Data transfer
• Compute
infrastructure
• Deploy and serve the
model
• Model dependencies
• Feedback loop
• Application
architecture
• DevOps toolchain
A subset of tasks in Microsoft Team Data Science Process Lifecycle (TDSP)
43. Deep learning
Specify a structure and a
loss function
Optimize using gradient
descent
Network feeds forward
with matrix multiplications
and point-wise activations
Network backpropagates
using multivariate chain
rule
Update the weights
accordingly
Optimize structure
Prevent over or under
fitting
Converge to a high-
quality local minima
Use the right loss function
Effective learning rate
Appropriate data
augmentation
Proper pre-processing
44. Transfer Learning
1. Train on
Imagenet
3. Medium
dataset: finetuning
2. Small dataset:
feature extractor
Freeze
these
Train this
more data = retrain
more of the network
(or all of it)
Freeze
these
Train this
45. Use pre-built solutions
http://aka.ms/cisolutions
Reference architecture for common
scenarios
Built on best practice design patterns
Automated deployment on
your Azure subscription
Customizable for your needs
Supported by a global partner
ecosystem
47. R-CNN
• Extract possible objects using a region proposal method
(the most popular one being Selective Search)
• Extract features from each region using a CNN
• Classify each region with SVMs
https://arxiv.org/abs/1311.2524
48. Fast R-CNN
• An input image and multiple regions of interest
(ROI’s) are input into a fully convolutional
network.
• Each ROI is pooled into a fixed-size feature
map and then mapped to a feature vector by
fully connected layers (FCs).
• The network has two output vectors per RoI:
softmax probabilities and per-class bounding-
box regression offsets.
• The architecture is trained end-to-end with a
multi-task loss.
https://arxiv.org/abs/1504.08083
• Used Selective Search to generate object proposals,
but instead of extracting all of them independently
and using SVM classifiers, it applied the CNN on the
complete image
• Used both Region of Interest (ROI) Pooling on the
feature map with a final feed forward network for
classification and regression.
50. Faster R-CNN
• A Region Proposal Network (RPN) that shares
full-image convolutional features with the
detection network, thus enabling nearly cost-
free region proposals.
• An RPN is a fully convolutional network that
simultaneously predicts object bounds and
objectness scores at each position.
• The RPN is trained end-to-end to generate
high-quality region proposals, which are used
by Fast R-CNN for detection.
• RPN and Fast R-CNN are merged into a single
network by sharing their convolutional features,
with “attention” mechanisms.
https://arxiv.org/abs/1506.01497
image
conv layers
feature
maps
Region
Proposal
Network
classifier
ROI pooling
51. Grocery item object detection and classification
• Automated grocery inventory
management in connected
refrigerators
• Implemented Fast R-CNN object
detection in CNTK. REST API published
using Python Flask in Azure
• Annotated 311 images, split into 71 test
and 240 training images. In total 2578
annotated objects, i.e. on average 123
examples per class
• Prototype classifier has a precision of
98% at a recall of 80%, and 93%
precision at recall of 90%
https://blogs.technet.microsoft.com/machinelearning/2016/09/02/microsoft-and-liebherr-collaborating-on-new-generation-of-smart-refrigerators/
AI intelligently senses, processes, and acts on information—learning and adapting over time. We believe that, when designed with people at the center, AI can extend your capabilities, free you up for more creative and strategic endeavors, and help you or your organization achieve more.
Innovations that extend your capabilities
Intelligence infused into products like Office 365, Cortana, Bing and Skype are helping millions of people save time and be more productive. Whether you’re looking to break down language barriers or bring professional design to your presentations, Microsoft AI can extend your capabilities today.
Powerful platform that makes AI accessible
Built on breakthrough advances in AI research and the power of the cloud, we’re delivering a flexible platform for organizations and developers to infuse intelligence into their products and services using tools and services like Microsoft Cognitive Services, Azure Machine Learning, and the Bot Framework.
Trusted approach that puts you in control
Our transparent approach to AI puts your privacy first. Built on our enterprise-grade security practices, it helps protect your information and puts you in control. Our principles lead with ethics, accountability, and inclusive design to empower people and organizations, and positively impact society.
Please go visit microsoft.com/ai to learn more about the overall approach. Today we will focus on various technologies in the AI platform.
AI intelligently senses, processes, and acts on information—learning and adapting over time. We believe that, when designed with people at the center, AI can extend your capabilities, free you up for more creative and strategic endeavors, and help you or your organization achieve more.
Innovations that extend your capabilities
Intelligence infused into products like Office 365, Cortana, Bing and Skype are helping millions of people save time and be more productive. Whether you’re looking to break down language barriers or bring professional design to your presentations, Microsoft AI can extend your capabilities today.
Powerful platform that makes AI accessible
Built on breakthrough advances in AI research and the power of the cloud, we’re delivering a flexible platform for organizations and developers to infuse intelligence into their products and services using tools and services like Microsoft Cognitive Services, Azure Machine Learning, and the Bot Framework.
Trusted approach that puts you in control
Our transparent approach to AI puts your privacy first. Built on our enterprise-grade security practices, it helps protect your information and puts you in control. Our principles lead with ethics, accountability, and inclusive design to empower people and organizations, and positively impact society.
Please go visit microsoft.com/ai to learn more about the overall approach. Today we will focus on various technologies in the AI platform.