TrainingArtificial intelligence / Data & AIAI-300
AI-300Microsoft · authorised trainingLeads to an exam
Operationalize machine learning and generative AI solutions
This course prepares professionals to implement and operationalize MLOps and GenAIOps solutions on Azure, covering both traditional machine learning models and generative AI applications built on Microsoft Foundry. Learners practice automation, CI/CD, infrastructure as code, and observability using tools such as GitHub Actions, Azure CLI, and Bicep. The focus is on collaborating with data science and DevOps teams to deliver production-ready AI systems.
- Duration
- 4 days
- Level
- Intermediate
- Provider
- Microsoft
- Topic
- Artificial intelligence / Data & AI
Course outline
- Learning path: Operationalize machine learning models (MLOps)
- Get started with machine learning in Azure
- Experiment with Azure Machine Learning
- Run training scripts and track models with MLflow in Azure Machine Learning
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
- Automate model training with GitHub Actions
- Deploy and monitor a model in Azure Machine Learning
- Learning path: Operationalize generative AI applications (GenAIOps)
- 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
Skills you will gain
- Designing machine learning training solutions using Azure Machine Learning services
- Comparing models using automated machine learning, MLflow, and the Responsible AI dashboard
- Converting notebooks to training scripts and tracking models with MLflow
- Optimizing model training by tuning hyperparameters
- Automating multistep machine learning workflows with Azure Machine Learning pipelines
- Automating model training with GitHub Actions
- Deploying, monitoring, and rolling back models using protected environments
- Managing prompts, evaluating, monitoring, and debugging AI agents in Microsoft Foundry
Who should attend
- Data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure
- Learners preparing to implement MLOps and GenAIOps workflows using Azure-native services
Prerequisites
- Experience with Python programming
- Foundational understanding of machine learning concepts
- Basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools
Certification
Exam
AI-300 - Microsoft Certified: Machine Learning Operations Engineer Associate
This course prepares you for this exam.
4000 PLN
Ask about dates