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

 

Course 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.

Course Content

  • 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

Prices & Delivery methods

Online Training

Duration
1 day

Price
  • on request
Classroom Training

Duration
1 day

Price
  • on request

Click on town name or "Online Training" to book Schedule

Europe

Germany

Berlin
Frankfurt
Munich
Berlin
Berlin

Italy

Online Training Time zone: Central European Time (CET) Course language: Italian
Online Training Time zone: Central European Summer Time (CEST) Course language: Italian
Online Training Time zone: Central European Summer Time (CEST) Course language: Italian
Online Training Time zone: Central European Time (CET) Course language: Italian

Switzerland

Zurich
Zurich
Zurich
Zurich
Zurich

United Kingdom

Online Training Time zone: Greenwich Mean Time (GMT) Course language: English
Instructor-led Online Training:   This computer icon in the schedule indicates that this date/time will be conducted as Instructor-Led Online Training.
This is a FLEX course, which is delivered both virtually and in the classroom.