Length
3 days
Version
A

Overview

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 in Microsoft Azure.

Please note: Microsoft has retired Azure training from the Software Assurance Training Voucher (SATV) catalogue. From 1st February 2020 we are no longer able to accept SATVs for this course.

Key Topics

Detailed Info
  • Introduction to Azure Machine Learning
  • Running Experiments and Training Models
  • Orchestrating Operations with Pipelines
  • Training Optimal Models
  • Monitoring Models
  • No-Code Machine Learning with Designer
  • Working with Data
  • Compute Contexts
  • Deploying and Consuming Models
  • Interpreting Models
Skills Gained
Key Topics
Target Audience
Prerequisites

Skills Gained

After completing this course, students will be able to:

  • Know how Azure services can support and augment the data science process
  • Use Azure Machine Learning service to automate the data science process end to end
  • Know about the machine learning pipeline and how the Azure Machine Learning service’s AutoML and HyperDrive can automate some of the laborious parts of it
  • Automatically manage and monitor machine learning models in the Azure Machine Learning service

Key Topics

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

Lab : Creating an Azure Machine Learning Workspace

Lab : 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

Lab : Creating a Training Pipeline with the Azure ML Designer

Lab : 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
  • Lab : Running Experiments
  • Lab : 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

Lab : Working with Datastores

Lab : 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

Lab : Working with Environments

Lab : 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

Lab : Creating a Pipeline

Lab : 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

Lab : Creating a Real-time Inferencing Service

Lab : 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

Lab : Tuning Hyperparameters

Lab : 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

Lab : Reviewing Automated Machine Learning Explanations

Lab : 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

Lab : Monitoring a Model with Application Insights

Lab : Monitoring Data Drift

Target Audience

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.

We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please call us at +632 8244 2098 or email [email protected]

Prerequisites

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.

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

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The supply of this course by DDLS is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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Virtual Classroom
September 13 2021 - September 15 2021
November 3 2021 - November 5 2021

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