ML Platforms & Deployment
Train, deploy and operate models on Azure ML, AWS, Vertex AI and ML.NET.
Beginner
1 courseIntermediate
8 coursesAzure ML Workspace Fundamentals
Azure ML workspace architecture, compute, datasets, environments, governance, security and cost.
Azure ML Studio and SDK – Overview
Navigate Azure ML Studio, notebooks, the Python SDK v2 and CLI v2 with a first end-to-end job.
Azure ML: Pipelines and Experiment Tracking
Build Azure ML pipelines, track experiments with MLflow and register and version the best model.
Azure ML: Practical Use Cases
Choose the right technique and run classification, clustering and batch inference with AutoML and the Designer.
Deploying Models with Azure Machine Learning
Online and batch endpoints, scoring scripts, blue/green deployment, AKS and model monitoring on Azure ML.
Deploying Machine Learning Solutions
Deploy models with Flask, on serverless, on Google AI Platform and to AWS SageMaker.
Building Apps with Machine Learning in .NET
Add data and image classification to a Blazor app with ML.NET and Model Builder.
Building Apps with ML.NET
Use ML.NET for data and image classification in a real .NET (Wired Brain Coffee) application.
Interested in ML Platforms & Deployment?
Contact us to book a course or get a custom training plan for your team.