This session will explore fears around the use of data and how strategic data governance can give people the confidence to be curious and explore data.

Learn how to work with compute targets and environments in the Azure Machine Learning workspace.

Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.

Learn how to work with data in Azure Machine Learning. Whether you want to access data in notebooks or scripts, you can read data directly, through datastores, or data assets.

Tables in a Microsoft Fabric lakehouse are based on the Delta Lake storage format commonly used in Apache Spark. By using the enhanced capabilities of delta tables, you can create advanced analytics solutions.

Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

Learn how to train, test, and deploy a machine learning model by using environments as part of your machine learning operations (MLOps) strategy.

Explore the use of generative artificial intelligence (AI) models for natural language processing (NLP) in Azure Machine Learning.

Learn how to automate code checks whenever you update code for machine learning workloads.

Designing reports for enterprise scale requires more than just connecting to data. Understanding semantic models and strategies for scalability and optimization are key to a successful enterprise implementation. This learning path helps you prepare for the Fabric Analytics Engineer Certification.

Learn how to manipulate and visualize categorical data using pandas and seaborn.

This course introduces key concepts to proficiently work with data - data cleaning, preprocessing, manipulation, transformation, and core concepts of data visualization and interpretation. You'll also work through examples with a provided dataset.