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

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Duration

4 Days

24 CPD hours

This course is intended for

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.

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.

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.

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.
  • 1 - 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 solution

    2 - Design a machine learning model training solution

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

    3 - Design a model deployment solution

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

    4 - Design a machine learning operations solution

    • Explore an MLOps architecture
    • Design for monitoring
    • Design for retraining

    5 - 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

    6 - Explore developer tools for workspace interaction

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

    7 - Make data available in Azure Machine Learning

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

    8 - Work with compute targets in Azure Machine Learning

    • Choose the appropriate compute target
    • Create and use a compute instance
    • Create and use a compute cluster

    9 - Work with environments in Azure Machine Learning

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

    10 - Find the best classification model with Automated Machine Learning

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

    11 - Track model training in Jupyter notebooks with MLflow

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

    12 - 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

    13 - Track model training with MLflow in jobs

    • Track metrics with MLflow
    • View metrics and evaluate models

    14 - 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

    15 - Run pipelines in Azure Machine Learning

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

    16 - Register an MLflow model in Azure Machine Learning

    • Log models with MLflow
    • Understand the MLflow model format
    • Register an MLflow model

    17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

    • Understand Responsible AI
    • Create the Responsible AI dashboard
    • Evaluate the Responsible AI dashboard

    18 - 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

    19 - 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
    Training Insurance Included!

    When you organise training, we understand that there is a risk that some people may fall ill, become unavailable. To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.

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    Global Schedule

    GTR = Guarenteed to Run

    28 May 24 Enquire Book
    14:00 - 22:00Live Online1,785
    Exam VoucherBook Now
    FlexibleVarious165

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