DP-3007 Train and deploy a machine learning model with Azure Machine Learning

Tässä Microsoft Applied Skills -koulutuksessa perehdytään ML -mallien tekoon Azuressa. Koulutuksen lopussa suoritetaan Applied Skills Assessment, jolla voi osoittaa osaamisensa.

Tavoite

Opi kuinka käytät Azure Machine Learnig Workspacea.

Kenelle

Koulutus on suunnattu ML-asiantuntijoille.

Koulutuksen sisältö

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.

• Access data by using Uniform Resource Identifiers (URIs).
• Connect to cloud data sources with datastores.
• Use data asset to access specific files or folders.

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.

• Choose the appropriate compute target.
• Work with compute instances and clusters.
• Manage installed packages with environments.

Work with environments in Azure Machine Learning

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

• Understand environments in Azure Machine Learning.
• Explore and use curated environments.
• Create and use custom environments.

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.

• Convert a notebook to a script.
• Test scripts in a terminal.
• Run a script as a command job.
• Use parameters in a command job.

Track model training with MLflow in jobs

Learn how to track model training with MLflow in jobs when running scripts.

• Use MLflow when you run a script as a job.
• Review metrics, parameters, artifacts, and models from a run. 

Register an MLflow model in Azure Machine Learning

Learn how to log and register an MLflow model in Azure Machine Learning.

• Log models with MLflow.
• Understand the MLmodel format.
• Register an MLflow model in Azure Machine Learning.

Deploy a model to a managed online endpoint

Learn how to deploy models to a managed online endpoint for real-time inferencing.

• Use managed online endpoints.
• Deploy your MLflow model to a managed online endpoint.
• Deploy a custom model to a managed online endpoint.
• Test online endpoint.

Assessment test

Avainsanat

Azure Machine Learning