We provide virtual course about DP-3007: Train and deploy a machine learning model with Azure Machine Learning in english. To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute.
Course description:
After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this course , you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
Additionally, you'll also learn to perform post-migration tasks like disaster recovery and monitoring for Azure SQL Database. These skills are essential for ensuring a smooth, efficient transition to Azure SQL Database, and maintaining its operation post-migration.
Course content:
Modulse 1 - Make data available in Azure Machine Learning:
• Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.
Modulse 2 - Work with compute targets in Azure Machine Learning:
• 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.
Modulse 3 - Work with environments in Azure Machine Learning:
• Learn how to use environments in Azure Machine Learning to run scripts on any compute target
Modulse 4 - Run a training script as a command job in Azure Machine Learning:
• Learn how to convert your code to a script and run it as a command job in Azure Machine Learning
Modulse 5 - Track model training with MLflow in jobs:
• Learn how to track model training with MLflow in jobs when running scripts
Modulse 6 - Register an MLflow model in Azure Machine Learning:
• Learn how to log and register an MLflow model in Azure Machine Learning
Modulse 7 - Deploy a model to a managed online endpoint:
• Learn how to deploy models to a managed online endpoint for real-time inferencing
Target audience:
Role: AI Engineer, Data Engineer, Developer, Data Scientist
Prerequisites:
• Familiarity with Azure services
• Experience with Azure Machine Learning and MLflow
• Experience performing tasks related to machine learning by using Python
Language:
• English course material, english speaking instructor