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


4 Days


Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running machine learning workloads on Microsoft Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

Course Objectives

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.

Who Should Attend?

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.

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Course Prerequisites

  • 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
  • AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.
  • Agenda

    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 sulution

    Design a machine learning model training solution

    • Identify machine learning tasks
    • Choose a service to train a machine learning model
    • Decide between compute options

    Design a model deployment sulution

    • Understand how model will be consumed
    • Decide on real-time or batch deployment

    Explore Azure Machine Learning workspace resources and assets

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

    Explore developer touls for workspace interaction

    • Explore the studio
    • Explore the Python SDK
    • Explore the CLI

    Make data available in Azure Machine Learning

    • Understand URIs
    • Create a datastore
    • Create a data asset

    Work with compute targets in Azure Machine Learning

    • Create and use a compute instance
    • Create and use a compute instance
    • Create and use a compute cluster

    Work with environments in Azure Machine Learning

    • Understand environments
    • Explore and use curated environments
    • Create and use custom environments

    Find the best classification model with Automated Machine Learning

    • Preprocess data and configure featurization
    • Run an Automated Machine Learning experiment
    • Evaluate and compare models

    Track model training in Jupyter notebooks with MLflow

    • Configure MLflow for model tracking in notebooks
    • Train and track models in notebooks

    Run a training script as a command job in Azure Machine Learning

    • Convert a notebook to a script
    • Run a script as a command job
    • Use parameters in a command job

    Track model training with MLflow in jobs

    • Track metrics with MLflow
    • View metrics and evaluate models

    Run pipelines in Azure Machine Learning

    • Create components
    • Create a pipeline
    • Run a pipeline job

    Perform hyperparameter tuning with Azure Machine Learning

    • Define a search space
    • Configure a sampling method
    • Configure early termination
    • Use a sweep job for hyperparameter tuning

    Deploy a model to a managed online endpoint

    • 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

    Deploy a model to a batch endpoint

    • 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

    Upcoming Class Dates and Times

    May 1, 2, 3, 4
    8:00 AM - 4:00 PM
    ENROLL $2,380.00

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