After this course you can automate the full lifecycle of traditional models in Azure Machine Learning and run generative applications in Microsoft Foundry. You will version prompts, measure answer quality, detect data drift and roll out new versions in a controlled way. You provision infrastructure with Bicep and GitHub Actions so that it can be repeated. It suits you if you come from data science, are comfortable with Python and want models to keep running reliably after their first release.
What you’ll take away.
- Provision Machine Learning workspaces using Bicep, Azure CLI and GitHub Actions
- Track experiments with MLflow and build training pipelines
- Roll out models progressively, monitor for data drift and roll back safely
- Version prompts and evaluate generative AI automatically
- Measurably improve RAG quality and track token costs
Your training in detail
- Who is it for?
- Data scientists and AI engineers who want to run models and AI applications reliably in production.
- Prerequisites
- You have data science and Python experience, and know DevOps basics such as GitHub Actions and working on the command line.
- Format & planning
- Standard length: 4 training days. A different length can be arranged for companies.
Agenda: from understanding to application
Our independently developed agenda proposal. Focus areas, lab environment and scope are agreed before booking.
Setting up workspaces, datastores, compute and permissions
Infrastructure as code with Bicep, Azure CLI and GitHub Actions
Orchestrating training: MLflow, AutoML, hyperparameters and pipelines
Registering, deploying and monitoring models in production
Foundry environments, model deployment and prompt versioning
Evaluation and observability for generative AI and agents
Optimising RAG and customising models through fine-tuning
You build a pipeline that trains, registers and progressively deploys a model when the repository changes. You also add an alert that triggers retraining when data drift is detected.
Exams, licences & credentials
This course prepares you for the AI-300 (Operationalizing Machine Learning and Generative AI Solutions) exam. The exam and its fee are included only if your offer confirms it. The Microsoft Certified: Machine Learning Operations Engineer Associate certification is awarded by Microsoft, not by NextNowa. This is not an official Microsoft course.
Vendor references & review date
Sources checked on 2026-11-04. These sources describe the technology or external exam requirements. They do not imply a partnership with NextNowa.
Your learning content
These objectives describe the training topic. The specific syllabus, dates and trainer are agreed before booking; the written offer is binding.
Frequently asked questions
In which language can I take the AI-300 exam?
Microsoft currently lists English only on the certification page. Check again before you register, as language availability can change.
How do MLOps and GenAIOps differ?
MLOps covers training, registering and running traditional models. GenAIOps adds topics such as prompt versions, answer evaluation, token costs and RAG.
Does AI-300 Operationalizing Machine Learning and Generative AI Solutions: MLOps and GenAIOps on Azure include a certificate?
This course prepares you for the AI-300 (Operationalizing Machine Learning and Generative AI Solutions) exam. The exam and its fee are included only if your offer confirms it. The Microsoft Certified: Machine Learning Operations Engineer Associate certification is awarded by Microsoft, not by NextNowa. This is not an official Microsoft course.