Optimizing Microsoft Azure AI Solutions
Microsoft's cloud-based platform Azure provides multiple AI services such as AzureML Compute Cluster, Azure HDInsight, Azure Databricks, Azure DevOps. In this course you will learn how to design, deploy, and optimize applications built with Microsoft AI Solutions.
What you'll learn
Cloud-based platform Microsoft Azure has multiple AI services which could be used to train your model for big data sets as well as to deploy your model as a web service. In this course, Optimizing Microsoft Azure AI Solutions, you will learn the foundational knowledge of how to train your machine learning models using Azure's services such AzureML Compute Cluster, Azure HDInsight, Azure Databricks, and Azure Data Science Virtual Machine. Next, you will discover how to optimize your storage by using Azure Premium blob storage service and data formats such as Pickle and Parquet. Finally, you will explore how to scale your machine learning models and manage end-to-end machine learning life cycle using the principle of MLOps. When you’re finished with this course, you will have the skills and knowledge of Mircosoft Azure's core AI services needed to design, deploy, and optimize your model.
Table of contents
- Module Introduction 3m
- Compute Target Types 3m
- Setting the Stage 1m
- Provision Resource Group in Azure 1m
- Provision Workspace in Azure 1m
- Provision Local Cloud Compute 1m
- DSVM: Provision a Virtual Machine 3m
- DSVM: Attach as Compute Target 1m
- DSVM: Train a Model 3m
- Azure HDInsight: Provision a Cluster 2m
- Azure HDInsight: Attach as Compute Target 2m
- Azure HDInsight: Train a Model 2m
- Azure Databricks: Provision and Attach as Compute Target 3m
- AML Compute: Provision and Train a Deep Learning Model 7m
- Summary 1m
- Introduction 1m
- Current Challenges 1m
- What Is MLOps? 1m
- Benefits of MLOps 2m
- MLOps in Azure 1m
- Demo: Implement MLOps Using Azure 2m
- Build: Create Project and Repository 3m
- Build: Create Azure Pipeline 1m
- Build: Provision Environment, Resource Group, and Workspace 2m
- Build: Perform Data Quality Checks 1m
- Build: Train, Evaluate, Test, and Score Model 6m
- Build: Publish Artifacts 2m
- Execute the Build Phase 3m
- Release: Prepod and Prod Environment on ACS 5m
- Retrain: Schedule and Trigger 2m
- Summary 1m
About the author
Ranjan Relan is a Founder/CTO of GenAI company - AgentAnalytics.com . He previously worked as AI, Data and Tech Strategy Consultant with more than 16+ years of experience in the field of Analytics which includes working on Machine Learning, Big data and Data ware housing projects. He has helped clients across Hi Tech, Telecom and Pharma domain in developing data strategy, cloud adoption strategy, analytics strategy ,solution architecture.