AI-300 Operationalize Machine Learning and Generative AI Solutions

Duration: 4 days

Design and Build MLOps Infrastructure

Create and manage resources in a machine learning workspace
Manage assets in the workspace: data, environments, compute
Infrastructure as code for machine learning with Bicep and the Azure CLI

Model Lifecycle and Operations

Orchestrate and automate model training
Register and version models
Deploy models to production environments
Monitor and maintain models in operation

GenAIOps Infrastructure

Implement Foundry environments and platform configuration
Deploy and manage foundation models for production workloads
Version prompts and manage them through source control

Quality Assurance and Observability for Generative AI

Set up evaluation and validation for generative applications and agents
Implement observability for generative applications and agents

Optimise Generative Systems and Model Performance

Improve RAG performance and answer quality
Advanced fine-tuning and model adaptation

Learning Solution	Blended Learning, Firmenseminar, Individualcoaching, Klassenraumtraining, Online Live Webinar, Prüfungsvorbereitung
Language	Deutsch, Englisch, Französisch, Italienisch
Dates	2026/10/27, 2027/02/15, 2027/06/07, flexibel, auf Anfrage
Location	Brüttisellen, Lausanne, flexibel, auf Anfrage

Data scientists, ML engineers and DevOps professionals who make machine learning and generative AI solutions on Azure production-ready and operate them. Python experience is required, as well as a basic understanding of machine learning and DevOps fundamentals (source control, CI/CD, CLI). Knowledge of Azure Machine Learning, Microsoft Foundry, GitHub Actions and Bicep is helpful.

Build a reproducible MLOps environment using infrastructure as code
Automate training, registration, versioning and deployment of models
Monitor models and generative applications in operation
Embed evaluation, observability and prompt versioning for GenAI
Optimise RAG quality and fine-tuning in a targeted way
Be prepared for the AI-300 exam

AI-300 Operationalize Machine Learning and Generative AI Solutions

Design and Build MLOps Infrastructure

Model Lifecycle and Operations

GenAIOps Infrastructure

Quality Assurance and Observability for Generative AI

Optimise Generative Systems and Model Performance

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