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Global ai night sept 2019 - Milwaukee
- 1. September 2019 Cameron Vetter – Principal Cloud Developer, SafeNet Consulting @poshporcupine
- 3. Agenda 6:00 pm - Welcome / Food 6:15 pm - Keynote 6:30 pm - Group Session A lap around AI in Microsoft Azure 7:00 pm - Workshop Beginner Track 1 Creating applications that can see, hear, speak or understand 7:00 pm - Workshop Intermediate Track 1 Train machine learning models using automated machine learning 8:00 pm - Workshop Beginner Track 2 Is that wine good or bad? 8:00 pm - Workshop Intermediate Track 2 - Crash course on building and accelerating deep learning solutions
- 4. Thank You to our Sponsor! SafeNet specializes in being partners in your success. SafeNet currently focus on Custom Application Development, Cloud Consulting Services, and Data & Analytics.
- 5. Cameron Vetter Principal Cloud Consultant SafeNet Consulting Software Development is my passion. I have 20 years of experience using Microsoft tools and technologies to develop software. I have experience in many roles including Development, Architecture, Infrastructure, Management, and Leadership roles. I've worked for some of the largest companies in the world and for small local companies getting a breadth of experience in different Corporate Cultures. Currently, I am the Principal Cloud Architect at SafeNet Consulting, where I get to do what I love... Architect, Design, and Develop great software! I currently focus on Microservices, SOA, Azure, Cognitive Toolkit, and Kubernetes.
- 6. Ryan Bennett Managing Director SafeNet Consulting I have been a software engineer for 10 years and have worked as a consultant for six. I have worked at nearly 20 clients building anything from enterprise data warehouses to a startup app for filmmaking. I also am a founder and instructor at a not-for-profit organization that teaches full- stack web development to high school students during the summer.
- 7. Welcome from Microsoft Join Henk Boelman, Amy Boyd, Seth Juarez, and Eric Boyd for a warm welcome to the Global AI Night 2019, a dialogue about the latest and greatest from Azure AI, interesting behind the scenes stories, and some exciting news ahead.
- 9. 3.9$ TGlobal business value derived from AI in 2022 will reach “Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”, Gartner, April 2018. Decision support Virtual agents Decision automation Smart products 3.9$ T
- 10. Machine Learning on Azure Domain specific pretrained models To reduce time to market Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPytorch Onnx Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn PyCharm Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
- 11. Infuse apps with powerful, pre-trained AI models Customize easily and tailor to your needs Vision Speech Language Bing Search … Computer Vision | Video Indexer | Face | Content Moderator Speech to Text | Text to Speech | Speech Translation | Speaker Recognition Text Analytics | Spell Check | Language Understanding | Text Translation | QnA Maker Big Web Search | Video Search | Image Search | Visual Search | Entity Search | News Search | Autosuggest
- 12. Familiar Data Science tools Choose any python development environment And improve data science productivity PyCharm Jupyter Visual Studio Code Command lineZeppelin Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension
- 13. Build advanced deep learning solutions Use your favorite machine learning frameworks without getting locked into one framework ONNX Community project created by Facebook and Microsoft Use the best tool for the job. Train in one framework and transfer to another for inference TensorFlow PyTorch Scikit-Learn MXNet Chainer Keras
- 14. Frameworks Azure Create Deploy Services Devices Azure Machine Learning services Ubuntu VM Windows Server 2019 VM Azure Custom Vision Service ONNX Model Windows devices Other devices (iOS, etc.) Announcing ONNX Runtime open source
- 15. + To empower data science and development teams Develop models faster with automated machine learning Use any Python environment and ML frameworks Manage models across the cloud and the edge. Prepare data clean data at massive scale Enable collaboration between data scientists and data engineers Access machine learning optimized clusters Azure Machine Learning Python-based machine learning service Azure Databricks Apache Spark-based big-data service
- 16. Bring AI to everyone with an end-to-end, scalable, trusted platform Built with your needs in mind Support for open source frameworks Managed compute DevOps for machine learning Simple deployment Tool agnostic Python SDK Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere
- 17. Leverage your favorite deep learning frameworks AZURE ML SERVICE Increase your rate of experimentation Bring AI to the edge Deploy and manage your models everywhere TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer AZURE DATABRICKS Accelerate processing with the fastest Apache Spark engine Integrate natively with Azure services Access enterprise-grade Azure security
- 18. From the Intelligent Cloud to the Intelligent Edge Train and deploy Train and deploy Deploy Track models in production Capture model telemetry Retrain models
- 19. Accelerate deep learning General purpose machine learning D, F, L, M, H Series CPUs Optimized for flexibility Optimized for performance GPUs FPGAs Deep learning N Series Specialized hardware accelerated deep learning Project Brainwave
- 20. Vision Speech Language Microsoft AI breakthroughs
- 22. Machine Learning on Azure Domain specific pretrained models To reduce time to market Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPytorch Onnx Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn PyCharm Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
- 24. Track 1 – Beginner (1st Hour) Ryan Bennett – Managing Director / SafeNet Consulting
- 25. Track 1 – Beginner (2nd Hour) Ryan Bennett – Managing Director / SafeNet Consulting
- 26. Track 2 – Intermediate (1st Hour) Cameron Vetter – Principal Cloud Consultant / SafeNet Consulting @poshporcupine
- 27. Track 2 – Intermediate (2nd Hour) Cameron Vetter – Principal Cloud Consultant / SafeNet Consulting @poshporcupine
- 28. Thank You!
Notas del editor
- Gartner predicts that, in 2022, the global business value derived from AI will be $3.9 trillion. Decision support and augmentation will account for $1.7T. Virtual agents will account for $1T. Smart products will account for $624B. Decision automation systems will account for $546B. <Transition>: $3.9T. Let’s break that down.
- Our approach to ML frameworks is simple. We give customers the flexibility to choose their deep learning framework, without getting locked one framework. To help with this we’ve created a community project, ONNX, in partnership with Facebook that allows customers to train in one framework and use another one for inference Now, let me move to the ML services on Azure
- Custom vision
- Then next layer in the stack is the services that these frameworks run on. We have two main services to help customers of all types do machine learning. Azure Machine learning is a Python-based machine learning service. It’s can be accessed from any Python development environment. With automated machine learning capabilities, data scientists can build models faster. DevOps for machine learning enables data scientists and developers to enhance productivity with experiment tracking, model management and monitoring, integrated CI/CD, and machine learning pipelines. Models then can be deployed and managed in the cloud, on-premises and the edge. Azure Databricks is an Apache Spark-based big-data service with Azure Machine Learning integration. It also has interactive notebooks that enable collaboration between data scientists and data engineers. Azure Databricks enables data scientists coming from a big data and Spark based background to prep and clean data and develop machine learning models using the language of their choice. Azure Databricks & Azure Machine Learning work together nicely together and these two services enable data scientists of all types build and train machine learning models faster.
- Azure Machine Learning Services empowers you to bring AI to everyone with an end-to-end, scalable, trusted platform. Boost your data science productivity Python pip-installable extensions for Azure Machine Learning that enable data scientists to build and deploy machine learning and deep learning models Now available for Computer Vision, Text Analytics and Time-Series Forecasting. Increase your rate of experimentation Rapidly prototype on your desktop, then easily scale up on virtual machines or scale out using Spark clusters Proactively manage model performance, identify the best model, and promote it using data-driven insights Collaborate and share solutions using popular Git repositories. Deploy and manage your models everywhere Use Docker containers to deploy models into production faster in the cloud, on-premises, or at the edge Promote your best performing models into production and retrain them when their performance degrades Azure Machine Learning Services are built with your needs in mind, providing: GPU-enabled virtual machines Low-latency predictions at scale Integration with popular Python IDEs Role-based access controls Model versioning Automated model retraining (Optional: other services) Azure Machine Learning Workbench integrates with ONNX models Work with your ONNX models from Visual Studio Code Tools for AI. Build deep learning models and call services straight from your favorite IDE easier with Azure Machine Learning services built right in. Create a seamless developer experience across desktop, cloud, or at the edge. AI Toolkit for Azure IoT Edge MMLSpark is an open-source Spark package that enables you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets by using deep learning and data science tools for Apache Spark. Azure Machine Learning Services seamlessly integrates with the rest of the Azure portfolio. <Transition>: Azure Machine Learning Services allows you to deploy models to many different production environments.
- Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Spark and Azure to help customers accelerate innovation. With Azure Databricks, you can: Accelerate data processing with the fastest Spark engine Innovate faster, thanks to native integration with services like PBI, Azure SQL DW, Cosmos DB and Blob Storage Protect your data with enterprise-grade Azure security. Azure Machine Learning services enables you to: Bring the power of AI to the IoT edge Increase your rate of experimentation by rapidly prototyping on your desktop, then easily scaling up on VMs or scaling out on Spark clusters Deploy models into production faster in the cloud, on-premises, or at the edge Promote your best performing models into production and retrain them when their performance degrades These Azure services also empower you to leverage your favorite deep learning frameworks for AI development, including: TensorFlow The Microsoft Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer <Transition>: Let’s dive into each of these services in a little more detail, starting with Azure Databricks.
- Let’s move to the last part of our Machine Learning portfolio. We are the only company that offers the ability to deploy and manage models, whether in the cloud, on-premises, or even the Edge. This is extremely valuable in disconnected scenarios, where predictions have to be made on the Edge, without connectivity to the cloud. With IoT deployments becoming more widespread, we are well positioned to help our customers innovate with AI wherever they want.
- For machine learning and deep learning, you need powerful hardware We have the most comprehensive AI infrastructure From general purpose CPUs to specialized HW (FPGAs) FPGA offer lowest cost inferencing. Lower than Google’s TPUs. You are also not locked into one framework. We also have the most comprehensive set of GPU options so customers can choose the right one for their project. (best price/performance) Let me move to the last part of our ML portfolio.
- Microsoft Research has made significant breakthroughs in the AI categories of Vision, Speech and Language In fact, we were the first to reach parity with humans in object recognition, speech recognition, machine translation and machine reading comprehension These breakthroughs are not enough by themselves. You’ve been a critical part of driving adoption of AI with our customers.
- Thank you! Because of your efforts we have driven significant momentum in FY18. 1M developers using Azure Cognitive Services Over 300k developers using Azure Bot Service Great adoption of our machine learning offerings both first party and third party In FY19, we are looking to accelerate our growth.
- <Transition>: Now, at this point, you’re probably wondering, “how do I get started?”