Operationalize machine learning and generative AI solutions
AI-300(4 days)
Operationalize machine learning and generative AI solutions
18.800 DKK
(excl. VAT)
Course calendar
Description
Training a model is one thing; getting it to run reliably in production is a different discipline entirely. AI-300 brings the two worlds together under the term AIOps: Machine Learning Operations (MLOps) for classic ML models and Generative AI Operations (GenAIOps) for generative applications and agents. The course is built around exactly that transition, from experiments in a notebook to production-ready AI systems with automation, version control, and monitoring.
You first learn to design and build the infrastructure that MLOps rests on. This covers Azure Machine Learning workspaces, compute, datastores, and ML assets, as well as identity and access management with managed identities and role-based access control (RBAC), network security, and private networking. From there, you work with the entire model lifecycle: training, optimization, deployment to endpoints, and automating the process through CI/CD with GitHub Actions and infrastructure as code with Bicep and Azure CLI.
On the generative side, the focus shifts to Microsoft Foundry. You learn to design and implement GenAIOps infrastructure and to deploy generative AI applications and agents so they can be put into production in a controlled way. A central part is quality assurance and observability: how to evaluate generative solutions, set up monitoring, and catch problems such as drift, latency, and unexpected behavior before they reach users.
Finally, we look at optimization. You work on improving both the quality of generative systems and their performance and costs, so the solutions don't just work but are also economically and operationally sustainable in an enterprise environment. Throughout, there is an emphasis on collaboration with data scientists and DevOps teams, because production-ready AI is rarely a solo exercise.
After the course, you can:
- Design and implement MLOps infrastructure on Azure with Azure Machine Learning
- Configure identity and access management with managed identities and RBAC, and secure networks with private networking
- Manage the lifecycle of ML models from training and optimization to deployment on endpoints
- Automate ML workflows with CI/CD via GitHub Actions and infrastructure as code with Bicep and Azure CLI
- Design and implement GenAIOps infrastructure and deploy generative AI applications and agents with Microsoft Foundry
- Set up quality assurance and observability for generative solutions, including evaluation and monitoring
- Optimize the quality, performance, and costs of generative AI systems in production
The course is aimed at data scientists, machine learning engineers, and DevOps professionals who want to deliver production-ready AI solutions on Azure. It requires experience with Python, a basic understanding of machine learning, and familiarity with DevOps practices such as source control, CI/CD, and command-line tools.
Related exam
Prerequisites
To get the most out of the course, we recommend experience or knowledge equivalent to:
Certification bundles
Module overview
Learn the full MLOps lifecycle for machine learning models, from experimentation and pipeline automation to CI/CD, automated testing, and model deployment in production.
Lessons:
- Experiment with Azure Machine Learning
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
- Trigger Azure Machine Learning jobs with GitHub Actions
- Trigger GitHub Actions with feature-based development
- Work with environments in GitHub Actions
- Deploy a model with GitHub Actions
Learn the full GenAIOps lifecycle for generative AI applications, from planning and prompt management to evaluation, automated testing, monitoring, and tracing in production.
Lessons:
- Plan and prepare a GenAIOps solution
- Manage prompts for agents in Microsoft Foundry with GitHub
- Evaluate and optimize AI agents through structured experiments
- Automate AI evaluations with Microsoft Foundry and GitHub Actions
- Monitor your generative AI application
- Analyze and debug your generative AI app with tracing
Free course advice
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