Machine Learning with Azure Databricks

Machine learning workflows in the cloud can be complex, fragmented, and hard to scale. This course will teach you how to build, manage, and automate end-to-end machine learning workflows using Databricks MLflow and Azure Machine Learning.

What you'll learn

Many organizations struggle with fragmented machine learning workflows, disconnected tools, and inconsistent model management practices. Without a unified approach, developing, tracking, and deploying machine learning models can become inefficient and error-prone.

In this course, Machine Learning with Azure Databricks, you’ll gain the ability to build, manage, and automate end-to-end machine learning workflows using Databricks MLflow and Azure Machine Learning.

First, you’ll explore the key components of the Databricks ML Runtime and MLflow, and understand how they integrate with Azure Machine Learning and AI services.

Next, you’ll discover how to preprocess data, train models using scikit-learn, log experiments with MLflow, and tune models using cross-validation and hyperparameter optimization.

Finally, you’ll learn how to register and deploy models, monitor model performance, automate retraining pipelines with Databricks Workflows, and orchestrate ML workflows using Azure Data Factory.

When you’re finished with this course, you’ll have the skills and knowledge of machine learning in Databricks and Azure needed to develop, deploy, and maintain production-grade ML workflows at scale.