Design a machine learning solution Design a data ingestion strategy for machine learning projects Introduction Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution Exercise: Design a data ingestion strategy Knowledge check

Design a machine learning model training solution Introduction Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options Exercise: Design a model training strategy Knowledge check

Design a model deployment solution Introduction Understand how model will be consumed Decide on real-time or batch deployment Exercise – Design a deployment solution

Design a machine learning operations solution Introduction Explore an MLOps architecture Design for monitoring Design for retraining Knowledge check

Explore developer tools for workspace interaction Introduction Explore the studio Explore the Python SDK Explore the CLI Exercise-Explore the developer tools Knowledge check

Make data available in Azure Machine Learning Introduction Understand URIs Create a datastore Create a data asset Exercise – Make data available Knowledge check

Work with compute targets in Azure Machine Learning Introduction Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster Exercise – Work with compute resources Knowledge check

Work with environments in Azure Machine Learning Introduction Understand environments Explore and use curated environments Create and use custom environments Exercise – Work with environments Knowledge check

Experiment with Azure Machine Learning Find the best classification model with Automated Machine Learning Introduction Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models Exercise – Find the best classification model Knowledge check

Track model training in Jupyter notebooks with MLflow Introduction Configure MLflow for model tracking in notebooks Train and track models in notebooks Exercise – Track model training Knowledge check

Optimize model training with Azure Machine Learning Run a training script as a command job in Azure Machine Learning Introduction Convert a notebook to a script Run a script as a command job Use parameters in a command job Exercise – Run a training script as a command job Knowledge check

Track model training with MLflow in jobs Introduction Track metrics with MLflow View metrics and evaluate models Exercise – Use MLflow to track training jobs Knowledge check

Perform hyperparameter tuning with Azure Machine Learning Introduction Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning Exercise – Run a sweep job Knowledge check

Run pipelines in Azure Machine Learning Introduction Create components Create a pipeline Run a pipeline job Exercise – Run a pipeline job Knowledge check

Manage and review models in Azure Machine Learning Register an MLflow model in Azure Machine Learning Introduction Log models with MLflow Understand the MLflow model format Register an MLflow model Exercise – Log and register models with MLflow Knowledge check

Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Introduction Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard Exercise – Explore the Responsible AI dashboard Knowledge check

Deploy and consume models with Azure Machine Learning Deploy a model to a managed online endpoint Introduction Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints Exercise – Deploy an MLflow model to an online endpoint Knowledge check

Deploy a model to a batch endpoint Introduction Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints Exercise – Deploy an MLflow model to a batch endpoint Knowledge check