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Course
- Cloud
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 | 2m 50s
- Compute Target Types | 2m 56s
- Setting the Stage | 45s
- Provision Resource Group in Azure | 1m
- Provision Workspace in Azure | 54s
- Provision Local Cloud Compute | 58s
- DSVM: Provision a Virtual Machine | 2m 37s
- DSVM: Attach as Compute Target | 1m 27s
- DSVM: Train a Model | 3m
- Azure HDInsight: Provision a Cluster | 2m 22s
- Azure HDInsight: Attach as Compute Target | 1m 54s
- Azure HDInsight: Train a Model | 2m 24s
- Azure Databricks: Provision and Attach as Compute Target | 2m 47s
- AML Compute: Provision and Train a Deep Learning Model | 6m 52s
- Summary | 40s
About the author
Ranjan Relan is a Data and Tech Strategy Consultant with 11+ years of experience in the field of Machine Learning, Big Data, Cloud Computing and Data warehousing.
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