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    Instructor-led Training

    Course Description

    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.

    Audience Profile

    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.

    Course Outline

    Course Outline

    Skills at a glance

    • Design and prepare a machine learning solution (20–25%)

    • Explore data, and train models (35–40%)

    • Prepare a model for deployment (20–25%)

    • Deploy and retrain a model (10–15%)

    Design and prepare a machine learning solution (20–25%)

    Design a machine learning solution

    • Determine the appropriate compute specifications for a training workload

    • Describe model deployment requirements

    • Select which development approach to use to build or train a model

    Manage an Azure Machine Learning workspace

    • Create an Azure Machine Learning workspace

    • Manage a workspace by using developer tools for workspace interaction

    • Set up Git integration for source control

    • Create and manage registries

    Manage data in an Azure Machine Learning workspace

    • Select Azure Storage resources

    • Register and maintain datastores

    • Create and manage data assets

    Manage compute for experiments in Azure Machine Learning

    • Create compute targets for experiments and training

    • Select an environment for a machine learning use case

    • Configure attached compute resources, including Apache Spark pools

    • Monitor compute utilization

    Explore data, and train models (35–40%)

    Explore data by using data assets and data stores

    • Access and wrangle data during interactive development

    • Wrangle interactive data with Apache Spark

    Create models by using the Azure Machine Learning designer

    • Create a training pipeline

    • Consume data assets from the designer

    • Use custom code components in designer

    • Evaluate the model, including responsible AI guidelines

    Use automated machine learning to explore optimal models

    • Use automated machine learning for tabular data

    • Use automated machine learning for computer vision

    • Use automated machine learning for natural language processing

    • Select and understand training options, including preprocessing and algorithms

    • Evaluate an automated machine learning run, including responsible AI guidelines

    Use notebooks for custom model training

    • Develop code by using a compute instance

    • Track model training by using MLflow

    • Evaluate a model

    • Train a model by using Python SDK v2

    • Use the terminal to configure a compute instance

    Tune hyperparameters with Azure Machine Learning

    • Select a sampling method

    • Define the search space

    • Define the primary metric

    • Define early termination options

    Prepare a model for deployment (20–25%)

    Run model training scripts

    • Configure job run settings for a script

    • Configure compute for a job run

    • Consume data from a data asset in a job

    • Run a script as a job by using Azure Machine Learning

    • Use MLflow to log metrics from a job run

    • Use logs to troubleshoot job run errors

    • Configure an environment for a job run

    • Define parameters for a job

    Implement training pipelines

    • Create a pipeline

    • Pass data between steps in a pipeline

    • Run and schedule a pipeline

    • Monitor pipeline runs

    • Create custom components

    • Use component-based pipelines

    Manage models in Azure Machine Learning

    • Describe MLflow model output

    • Identify an appropriate framework to package a model

    • Assess a model by using responsible AI principles

    Deploy and retrain a model (10–15%)

    Deploy a model

    • Configure settings for online deployment

    • Configure compute for a batch deployment

    • Deploy a model to an online endpoint

    • Deploy a model to a batch endpoint

    • Test an online deployed service

    • Invoke the batch endpoint to start a batch scoring job

    Apply machine learning operations (MLOps) practices

    • Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub

    • Automate model retraining based on new data additions or data changes

    • Define event-based retraining triggers

     

    Duration

    4 Days

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