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

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Module 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

Module 2 : Design a machine learning model training solution

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

Module 3 : Design a model deployment solution

• Understand how a model will be consumed.
• Decide whether to deploy your model to a real-time or batch endpoint.

Module 4 : Explore Azure Machine Learning workspace resources and assets

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

Module 5 : Explore developer tools for workspace interaction

• The Azure Machine Learning studio.
• The Python Software Development Kit (SDK).
• The Azure Command Line Interface (CLI).

Module 6 : Make data available in Azure Machine Learning

• Work with Uniform Resource Identifiers (URIs).
• Create and use datastores.
• Create and use data assets.

Module 7 : 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.

Module 8 : Work with environments in Azure Machine Learning

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

Module 9 : Find the best classification model with Automated Machine Learning

• Prepare your data to use AutoML for classification.
• Configure and run an AutoML experiment.
• Evaluate and compare models..

Module 10 : Track model training in Jupyter notebooks with MLflow

• Configure to use MLflow in notebooks
• Use MLflow for model tracking in notebooks

Module 11 : Run a training script 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.

Module 12 : Track model training with MLflow in jobs

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

Module 13 : Run pipelines in Azure Machine Learning

• Create components.
• Build an Azure Machine Learning pipeline.
• Run an Azure Machine Learning pipeline.

Module 14 : Perform hyperparameter tuning with Azure Machine Learning

• Define a hyperparameter search space.
• Configure hyperparameter sampling.
• Select an early-termination policy.
• Run a sweep job.

Module 15 : Deploy a model to a managed online endpoint

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

Module 16 : Deploy a model to a batch endpoint

• Create a batch endpoint.
• Deploy your MLflow model to a batch endpoint.
• Deploy a custom model to a batch endpoint.
• Invoke batch endpoints.

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Specifically:

• Creating cloud resources in Microsoft Azure.
• Using Python to explore and visualize data.
• Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
• Working with containers

To gain these prerequisite skills, take the following free online training before attending the course:

• Explore Microsoft cloud concepts.
• Create machine learning models.
• Administer containers in Azure

If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.