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Build Your First ML Model with Azure Machine Learning Service - PowerPoint PPT Presentation

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Build Your First ML Model with Azure Machine Learning Service Machine Intelligence Modern Infrastructure http://mi2.live What is MI2? MI2 Webinars focus on the convergence of machine intelligence and modern infrastructure and. Every

  1. Build Your First ML Model with Azure Machine Learning Service Machine Intelligence Modern Infrastructure http://mi2.live

  2. What is MI2? MI2 Webinars focus on the convergence of machine intelligence and modern infrastructure and. Every alternate week, I deliver informative and insightful sessions covering cutting-edge technologies. Each webinar is complemented by a tutorial, code snippets, and a video. MI2 strives to be an independent and neutral platform for exploring emerging technologies. Register at http://mi2.live

  3. Objectives • The lifecycle of a machine learning model • Overview of Azure Machine Learning service • Building blocks of Azure ML service • Demo • Summary

  4. Lifecycle of an ML model Testing Dataset Prediction Historical Training Algorithm Evaluation Model Data Dataset Production Data

  5. Lifecycle of ML Model Configur Deploy Scale Train Tune Provision e Developers / Data Scientists DevOps DevOps

  6. ML PaaS – Simplifying ML Model Management Dashboard CLI SDK Machine Learning PaaS Configur Deploy Scale Train Tune Provision e Developers / Data Scientists DevOps DevOps

  7. ML Model Management with Azure ML Service Portal CLI Python SDK Azure Machine Learning Service Configur Deploy Scale Train Tune Provision e Developers / Data Scientists DevOps DevOps

  8. What is Azure Machine Learning Service? • Managed service to build, train and deploy ML models • Integrates with Python environments, frameworks, and tools • Python APIs for end-to-end lifecycle management of models • Mix and match on-premises, cloud, and edge environments • Tightly integrated with Azure security, compliance, and virtual network support • AutoML for classic machine learning and deep learning • Includes experiment tracking, model management and monitoring, integrated CI/CD and machine learning pipelines

  9. What is Azure Machine Learning Service?

  10. What is Azure Machine Learning Service?

  11. The Big Picture of Azure ML Service

  12. A Closer Look at Azure ML Workspace Application Container Registry Storage Account Key Vault Insights

  13. What are we building? • An ML model based on Scikit-learn • Train the model locally • Use Azure ML Experiment to log metrics • Register fully trained model with Azure ML • Create a container image for the latest version of ML model • Launch the image as an Azure Container Instance for serving

  14. DEM O Building and deploying the machine learning model https://github.com/janakiramm/azureml-tutorial

  15. Summary • Azure ML service is a PaaS for machine learning • Empowers developers and data scientists to programmable lifecycle management of models • Workspace is the logical boundary for ML projects • Wide range of compute choices for model training • Comprehensive model management and experimentation • Container-based deployment as a web service or an edge module

  16. MI2 Sponsors

  17. Next Webinar Getting Started with AWS App Mesh AWS App Mesh makes it easy to run microservices by providing consistent visibility and network traffic controls for every microservice in an application. Attend this session to get a thorough overview of the AWS App Mesh. I will walk you through and end-to-end demo that leverages AWS App Mesh. Thursday, January 31st, 2019 9:00 AM PST / 10:30 PM IST Register at http://mi2.live

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