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Designing and Implementing a Data Science Solution on Azure

Price
2,380 USD
4 Days
MOC-DP-100T01-A
Classroom Training, Online Training
Microsoft

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  • Date: 9-Dec-2024 to 12-Dec-2024
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,380 USD
  • Date: 20-Jan-2025 to 23-Jan-2025
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,380 USD
  • Date: 3-Mar-2025 to 6-Mar-2025
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,380 USD
  • Date: 14-Apr-2025 to 17-Apr-2025
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,380 USD
  • Date: 7-Jul-2025 to 10-Jul-2025
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,380 USD
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Course Description

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 with Azure Machine Learning and MLflow.
 

Objectives


 

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.
 

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.
    • Working with containers To gain these prerequisite skills, take the following free online training before attending the course:
    • Explore Microsoft cloud concepts.
    • Create machine learning models.
    • Administer containers in AzureIf you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.

Topics

  • Design a machine learning solution Design a data ingestion strategy for machine learning projects
    • Introduction
    • Identify your data source and format
    • Choose how to serve data to machine learning workflows
    • Design a data ingestion solution
    • Exercise: Design a data ingestion strategy
    • Knowledge check
  • Design a machine learning model training solution
    • Introduction
    • Identify machine learning tasks
    • Choose a service to train a machine learning model
    • Decide between compute options
    • Exercise: Design a model training strategy
    • Knowledge check
    Design a model deployment solution
    • Introduction
    • Understand how model will be consumed
    • Decide on real-time or batch deployment
    • Exercise - Design a deployment solution
    Design a machine learning operations solution
    • Introduction
    • Explore an MLOps architecture
    • Design for monitoring
    • Design for retraining
    • Knowledge check
  • Explore and configure the Azure Machine Learning workspace Explore Azure Machine Learning workspace resources and assets
    • Introduction
    • Create an Azure Machine Learning workspace
    • Identify Azure Machine Learning resources
    • Identify Azure Machine Learning assets
    • Train models in the workspace
    • Exercise - Explore the workspace
    • Knowledge check
  • Explore developer tools for workspace interaction
    • Introduction
    • Explore the studio
    • Explore the Python SDK
    • Explore the CLI
    • Exercise-Explore the developer tools
    • Knowledge check
    Make data available in Azure Machine Learning
    • Introduction
    • Understand URIs
    • Create a datastore
    • Create a data asset
    • Exercise - Make data available
    • Knowledge check
    Work with compute targets in Azure Machine Learning
    • Introduction
    • Choose the appropriate compute target
    • Create and use a compute instance
    • Create and use a compute cluster
    • Exercise - Work with compute resources
    • Knowledge check
    Work with environments in Azure Machine Learning
    • Introduction
    • Understand environments
    • Explore and use curated environments
    • Create and use custom environments
    • Exercise - Work with environments
    • Knowledge check
  • Experiment with Azure Machine Learning Find the best classification model with Automated Machine Learning
    • Introduction
    • Preprocess data and configure featurization
    • Run an Automated Machine Learning experiment
    • Evaluate and compare models
    • Exercise - Find the best classification model
    • Knowledge check
  • Track model training in Jupyter notebooks with MLflow
    • Introduction
    • Configure MLflow for model tracking in notebooks
    • Train and track models in notebooks
    • Exercise - Track model training
    • Knowledge check
  • Optimize model training with Azure Machine Learning Run a training script as a command job in Azure Machine Learning
    • Introduction
    • Convert a notebook to a script
    • Run a script as a command job
    • Use parameters in a command job
    • Exercise - Run a training script as a command job
    • Knowledge check
  • Track model training with MLflow in jobs
    • Introduction
    • Track metrics with MLflow
    • View metrics and evaluate models
    • Exercise - Use MLflow to track training jobs
    • Knowledge check
    Perform hyperparameter tuning with Azure Machine Learning
    • Introduction
    • Define a search space
    • Configure a sampling method
    • Configure early termination
    • Use a sweep job for hyperparameter tuning
    • Exercise - Run a sweep job
    • Knowledge check
    Run pipelines in Azure Machine Learning
    • Introduction
    • Create components
    • Create a pipeline
    • Run a pipeline job
    • Exercise - Run a pipeline job
    • Knowledge check
  • Manage and review models in Azure Machine Learning Register an MLflow model in Azure Machine Learning
    • Introduction
    • Log models with MLflow
    • Understand the MLflow model format
    • Register an MLflow model
    • Exercise - Log and register models with MLflow
    • Knowledge check
  • Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
    • Introduction
    • Understand Responsible AI
    • Create the Responsible AI dashboard
    • Evaluate the Responsible AI dashboard
    • Exercise - Explore the Responsible AI dashboard
    • Knowledge check
  • Deploy and consume models with Azure Machine Learning Deploy a model to a managed online endpoint
    • Introduction
    • 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
    • Exercise - Deploy an MLflow model to an online endpoint
    • Knowledge check
  • Deploy a model to a batch endpoint
    • Introduction
    • 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
    • Exercise - Deploy an MLflow model to a batch endpoint
    • Knowledge check
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