Strategy of Experiments Design in Azure Machine Learning

Description

Master the design and execution of machine learning experiments in Azure. This course covers the Azure ML workspace, resource management, and the creation of classification and clustering pipelines using MLflow and Azure ML Pipelines, enabling professionals to build scalable, trackable, and reproducible ML solutions.

Indicative Duration: 4 training hours
*Duration is adjusted based on the final scope and the target audience.

Scope

1. Azure ML workspace
  • Data ingestion
  • Resources and assets
  • Developer tools
  • Compute targets
  • Environments
2. Experiments with ML
  • Solution design
  • Best classification model
  • MLflow
  • Pipelines for classification
  • Pipelines for clustering

Learning Objectives

Upon completion of the course participants will be able to:

  1. Navigate and utilize the Azure Machine Learning workspace, including managing data, compute resources, and environments.
  2. Design and execute machine learning experiments using Azure ML tools and pipelines.
  3. Apply MLflow for experiment tracking and model management.
  4. Build and orchestrate classification and clustering pipelines within Azure ML.
  5. Implement best practices for scalable and reproducible ML experimentation in a cloud environment.

Target Audience

  • Roles: Data Scientists, ML Engineers, Cloud Engineers, AI Specialists
  • Seniority Levels: Intermediate to Advanced professionals working with ML models and looking to scale experimentation in cloud environments

Prerequisite Knowledge

  • Basic understanding of machine learning concepts (classification, clustering)
  • Familiarity with Python programming
  • Experience with cloud platforms (preferably Azure)
  • Understanding of model training workflows
  • Exposure to version control and experiment tracking tools (e.g., MLflow) is a plus

Delivery Method

Sessions can be delivered via the following formats:

  • Live Online – Interactive virtual sessions via video conferencing
  • On-Site – At your organization’s premises
  • In-Person – At Code.Hub’s training center
  • Hybrid – A combination of online and in-person sessions

 

The training methodology combines presentations, live demonstrations, hands-on exercises and interactive discussions to ensure participants actively practice AI in realistic work scenarios.

Tags:

Date

On Demand

Organizer

Code.Hub
Email
[email protected]