Train and deploy a machine learning model with Azure Machine Learning [M-DP3007]
Startdata en plaatsen
computer Online: VIRTUAL TRAINING CENTER 22 apr. 2025Toon rooster event 22 april 2025, 10:00-18:00, VIRTUAL TRAINING CENTER, NL239160.1 |
computer Online: VIRTUAL TRAINING CENTER 24 jun. 2025Toon rooster event 24 juni 2025, 10:00-18:00, VIRTUAL TRAINING CENTER, NL239161.1 |
computer Online: VIRTUAL TRAINING CENTER 29 aug. 2025Toon rooster event 29 augustus 2025, 10:00-18:00, VIRTUAL TRAINING CENTER, NL239162.1 |
computer Online: VIRTUAL TRAINING CENTER 24 okt. 2025Toon rooster event 24 oktober 2025, 10:00-18:00, VIRTUAL TRAINING CENTER, NL239163.1 |
computer Online: VIRTUAL TRAINING CENTER 19 dec. 2025Toon rooster event 19 december 2025, 10:00-18:00, VIRTUAL TRAINING CENTER, NL239164.1 |
Beschrijving
Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge
Online of op locatie er is altijd een vorm die bij je past.
Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.
OVERVIEW
To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
OBJECTIVES
During this course, you will learn to:- Make data available in Azure Machine Learning
- Work with compute targets in Azure Machine Learning
- Work with environments in Azure Machine Learning
- Run a training script as a command job in Azure Machine…
Veelgestelde vragen
Er zijn nog geen veelgestelde vragen over dit product. Als je een vraag hebt, neem dan contact op met onze klantenservice.
Ontdek de verschillende trainingsmogelijkheden bij Global Knowledge
Online of op locatie er is altijd een vorm die bij je past.
Kies op welke manier jij of je team graag een training wilt volgen. Global Knowledge bied je verschillende trainingsmogelijkheden. Je kunt kiezen uit o.a. klassikaal, Virtueel Klassikaal (online), e-Learning en maatwerk. Met onze Blended oplossing kun je de verschillende trainingsvormen combineren.
OVERVIEW
To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
OBJECTIVES
During this course, you will learn to:- Make data available in Azure Machine Learning
- Work with compute targets in Azure Machine Learning
- Work with environments in Azure Machine Learning
- Run a training script as a command job in Azure Machine Learning
- Track model training with MLflow in jobs
- Register an MLflow model in Azure Machine Learning
- Deploy a model to a managed online endpoint
AUDIENCE
This entry-level course is proposed to technicians and Data scientists who plan to work with Microsoft Azure Machine Learning workspaces.CONTENT
Module 1 Make data available in Azure Machine Learning
Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.
Module 2 Work with compute targets in Azure Machine Learning
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
Module 3 Work with environments in Azure Machine Learning
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
Module 4 Run a training script as a command job in Azure Machine Learning
Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.
Module 5 Track model training with MLflow in jobs
Learn how to track model training with MLflow in jobs when running scripts.
Module 6 Register an MLflow model in Azure Machine Learning
Learn how to log and register an MLflow model in Azure Machine Learning.
Module 7 Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.
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Deel je ervaring
Heb je ervaring met deze cursus? Deel je ervaring en help anderen kiezen. Als dank voor de moeite doneert Springest € 1,- aan Stichting Edukans.Er zijn nog geen veelgestelde vragen over dit product. Als je een vraag hebt, neem dan contact op met onze klantenservice.