Microsoft Azure : Designing and Implementing a Data Science Solution on Azure / DP-100

Course Outline

Module 1: Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons:

Getting Started with Azure Machine Learning

Azure Machine Learning Tools

Hands-On: Creating an Azure Machine Learning Workspace

Hands-On: Working with Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Lessons:

Training Models with Designer

Publishing Models with Designer

Hands-On: Creating a Training Pipeline with the Azure ML Designer

Hands-On: Deploying a Service with the Azure ML Designer

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons:

Introduction to Experiments

Training and Registering Models

Hands-On: Running Experiments

Hands-On: Training and Registering Models

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons:

Working with Datastores

Working with Datasets

Hands-On: Working with Datastores

Hands-On: Working with Datasets

Module 5: Compute Contexts

One of the key benefits of the cloud is the ability to leverage compute resources on-demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons:

Working with Environments

Working with Compute Targets

Hands-On: Working with Environments

Hands-On: Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.

Lessons:

Introduction to Pipelines

Publishing and Running Pipelines

Hands-On: Creating a Pipeline

Hands-On: Publishing a Pipeline

Module 7: Deploying and Consuming Models

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons:

Real-time Inferencing

Batch Inferencing

Hands-On: Creating a Real-time Inferencing Service

Hands-On: Creating a Batch Inferencing Service

Module 8: Training Optimal Models

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons:

Hyperparameter Tuning

Automated Machine Learning

Hands-On: Tuning Hyperparameters

Hands-On: Using Automated Machine Learning

Module 9: Interpreting Models

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model’s behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

Lessons:

Introduction to Model Interpretation

using Model Explainers

Hands-On: Reviewing Automated Machine Learning Explanations

Hands-On: Interpreting Models

Module 10: Monitoring Models

After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

Lessons:

Monitoring Models with Application Insights

Monitoring Data Drift

Hands-On: Monitoring a Model with Application Insights

Hands-On: Monitoring Data Drift