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  • DP - 3007 : Train and deploy a machine learning model with Azure Machine Learning

This course guides you through training and deploying a machine learning model using Azure Machine Learning. You'll learn to set up your Azure Machine Learning workspace, prepare and configure compute resources, and train your model while tracking metrics with MLflow. The course also covers deploying the trained model to an online endpoint for real-time predictions.

Instructor

DP - 3007 : Train and deploy a machine learning model with Azure Machine Learning

Curriculum

Learn to train, evaluate, and deploy machine learning models using Azure Machine Learning in DP-3007. Master end-to-end ML workflows on the Azure platform.

Ratings

( 4.6 Ratings )

Live Online Classes starting on 01 January, 1970

COURSE OVERVIEW
DP - 3007 : Train and deploy a machine learning model with Azure Machine Learning

DP-3007: Train and Deploy Machine Learning Models with Azure Machine Learning certification is designed to validate an individual's skills in developing, managing, and deploying machine learning models using Azure Machine Learning services. This course teaches participants how to make data available and configure compute resources, train models, track metrics using MLflow, and deploy models to online endpoints for real-time predictions. Learners will explore setting up an Azure Machine Learning workspace, training models, and deploying them effectively. This certification is valuable for professionals leveraging Azure’s integrated tools for scalable machine learning, helping organizations enhance analytics capabilities and drive innovation securely and efficiently.

 

Audience Profile:

  • Data Scientists: Professionals focused on developing and implementing machine learning models.

  • Azure Machine Learning Users: Individuals working with Azure's machine learning tools and services.

 

Prerequisites:

  • None

 

Course Outline:

Module 1: Make data available in Azure Machine Learning

  • Access data by using Uniform Resource Identifiers (URIs)

  • Connect to cloud data sources with datastores

  • Use data assets to access specific files or folders

Module 2: Work with compute targets in Azure Machine Learning

  • Choose the appropriate compute target

  • Work with compute instances and clusters

  • Manage installed packages with environments

Module 3: Work with environments in Azure Machine Learning

  • Understand environments in Azure Machine Learning

  • Explore and use curated environments

  • Create and use custom environments

Module 4: Run a training script as a command job in Azure Machine Learning

  • Convert a notebook to a script

  • Test scripts in a terminal

  • Run a script as a command job

  • Use parameters in a command job

Module 5: Track model training with MLflow in jobs

  • Use MLflow when you run a script as a job

  • Review metrics, parameters, artifacts, and models from a run

Module 6: Register an MLflow model in Azure Machine Learning

  • Log models with MLflow

  • Understand the MLmodel format

  • Register an MLflow model in Azure Machine Learning

Module 7: Deploy a model to a managed online endpoint

  • Use managed online endpoints

  • Deploy your MLflow model to a managed online endpoint

  • Deploy a custom model to a managed online endpoint

  • Test online endpoints

4.6 Ratings
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