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

Ref. DP-3007
Duration:
1
 day
Exam:
Optional
Level:
Intermediate

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

Discover how to master Azure Machine Learning to optimize your artificial intelligence projects. Our course “Train and deploy a machine learning model with Azure Machine Learning (DP-3007)” guides you through the crucial steps from data preparation and compute resource configuration to training and deploying models for real-time predictions.

Whether you want to create and manage custom environments, monitor model performance with MLflow, or deploy your solutions effectively, this learning path is designed to equip professionals with the necessary skills to excel in the dynamic field of machine learning. Join our course ‘Train and deploy a machine learning model with Azure Machine Learning (DP-3007)’ to turn your knowledge into concrete actions and drive your AI projects to new heights of efficiency and innovation.”

Participant profiles

  • AI Engineer
  • Data Engineer
  • Developer
  • Data Scientist

Objectives

  • Make data accessible in Azure Machine Learning
  • Master compute targets in Azure Machine Learning
  • Run a training script as a command job in Azure Machine Learning
  • Track model training with MLflow
  • Register an MLflow model in Azure Machine Learning
  • Deploy a model to a managed online endpoint

Prerequisites

  • Knowledge of the data science process
  • Basic understanding of data science concepts
  • Familiarity with the Python language

Course content

Module 1 : Make data available in Azure Machine Learning

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

Module 2 : 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

Module 3 : Work with environments in Azure Machine Learning

  • Understand environments
  • 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
  • Run a script as a command job
  • Use parameters in a command job

Module 5 : Track model training with MLflow in jobs

  • Track metrics with MLflow
  • View metrics and evaluate models

Module 6 : Register an MLflow model in Azure Machine Learning

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

Module 7 : 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

Documentation

  • Digital course material included

Complementary courses

Temptraining funding

ITTA is a partner of Temptraining, the continuing education fund for temporary workers. This training fund can subsidize continuing education for anyone who works for an employer subject to the Collective Work Agreement (CCT) Rental of services.

Additional information

Discover how to make your data accessible with our course “Train and deploy a machine learning model with Azure Machine Learning (DP-3007)”

In a world where data is the new oil, knowing how to organize, store, and make available your precious data in Azure Machine Learning is essential. Our training guides you through the steps to understand URIs, create an efficient datastore, and manage your data assets optimally.

Optimize your projects with compute targets in Azure Machine Learning

Choosing the right compute target can make all the difference in executing your machine learning projects. Learn how to select the right compute target, create and use a compute instance, and a compute cluster to maximize the efficiency of your machine learning tasks.

Master environments in Azure Machine Learning

Understanding and navigating the various environments of Azure Machine Learning is crucial for the success of your projects. Our training covers everything from predefined environments to creating and using your own custom environments, thus speeding up your development.

Run training scripts efficiently

Transforming a notebook into a script and running it as a command job may seem complex, but our training makes this process transparent. You will also learn how to integrate parameters into your command jobs, thus optimizing your training scripts.

Track model training with MLflow

Using MLflow to track the metrics of your models and evaluate their performance is an invaluable skill. Our course shows you how to make the most of MLflow to keep track of your training and make informed decisions based on reliable data.

Register your models with MLflow in Azure Machine Learning

Discover how to register your MLflow models effectively in Azure Machine Learning, understand the MLflow model format, and ensure that your models are production-ready.

Deploy your models to managed online endpoints

Deploying your models to managed online endpoints is the final step in putting your work into production. Our training provides you with the tools and knowledge needed to successfully deploy your MLflow models and test online endpoints, ensuring that your models perform well and are ready to use.

Prix de l'inscription
CHF 850.-
Inclus dans ce cours
  • Training provided by a certified trainer
  • 180 days of access to Official Microsoft Labs
  • Official documentation in digital format
  • Official Microsoft achievement badge

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