Designing and Implementing a Data Science Solution on Azure

 

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.
 

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

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

Explore Azure Machine Learning workspace resources and assets

• Introduction
• Create an Azure Machine Learning workspace
• Identify Azure Machine Learning resources
• Identify Azure Machine Learning assets
• Train models in the workspace
• Exercise - Explore the workspace
• 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

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

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

Run pipelines in Azure Machine Learning

• Introduction
• Create components
• Create a pipeline
• Run a pipeline job
• Exercise - Run a pipeline job
• 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

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