DP-100T01 Designing and Implementing a Data Science Solution on Azure

1 – Design a data ingestion strategy for machine learning projects

• Identify your data source and format
• Choose how to serve data to machine learning workflows
• Design a data ingestion solution

2 – Design a machine learning model training solution

• Identify machine learning tasks
• Choose a service to train a machine learning model
• Decide between compute options

3 – Design a model deployment solution

• Understand how model will be consumed
• Decide on real-time or batch deployment

4 – Design a machine learning operations solution

• Explore an MLOps architecture
• Design for monitoring
• Design for retraining

5 – Explore Azure Machine Learning workspace resources and assets

• Create an Azure Machine Learning workspace
• Identify Azure Machine Learning resources
• Identify Azure Machine Learning assets
• Train models in the workspace

6 – Explore developer tools for workspace interaction

• Explore the studio
• Explore the Python SDK
• Explore the CLI

7 – Make data available in Azure Machine Learning

• Understand URIs
• Create a datastore
• Create a data asset

8 – Work with compute targets in Azure Machine Learning

• Choose the appropriate compute target
• Create and use a compute instance
• Create and use a compute cluster

9 – Work with environments in Azure Machine Learning

• Understand environments
• Explore and use curated environments
• Create and use custom environments

10 – Find the best classification model with Automated Machine Learning

• Preprocess data and configure featurization
• Run an Automated Machine Learning experiment
• Evaluate and compare models

11 – Track model training in Jupyter notebooks with MLflow

• Configure MLflow for model tracking in notebooks
• Train and track models in notebooks

12 – Run a training script as a command job in Azure Machine Learning

• Convert a notebook to a script
• Run a script as a command job
• Use parameters in a command job

13 – Track model training with MLflow in jobs

• Track metrics with MLflow
• View metrics and evaluate models

14 – Perform hyperparameter tuning with Azure Machine Learning

• Define a search space
• Configure a sampling method
• Configure early termination
• Use a sweep job for hyperparameter tuning

15 – Run pipelines in Azure Machine Learning

• Create components
• Create a pipeline
• Run a pipeline job

16 – Register an MLflow model in Azure Machine Learning

• Log models with MLflow
• Understand the MLflow model format
• Register an MLflow model

17 – Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

• Understand Responsible AI
• Create the Responsible AI dashboard
• Evaluate the Responsible AI dashboard

18 – Deploy a model to a managed online endpoint

• 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

19 – Deploy a model to a batch endpoint

• 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