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03 Days 03 Hours 03 Minutes 03 Seconds

This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices..

Learning Outcomes:

  • Design and implement an MLOps infrastructure

  • Implement machine learning model lifecycle and operations

  • Design and implement a GenAIOps infrastructure

  • Implement generative AI quality assurance and observability

Key Topics:

  • Create and manage assets in a Machine Learning workspace

  • Orchestrate model training

  • Implement model registration and versioning

  • Implement Foundry environments and platform configuration

  • Optimize retrieval-augmented generation (RAG) performance and accuracy

 

Exam Details

This course is designed to build participants’ understanding of key concepts and domains covered in the AI-300: Operationalizing Machine Learning and Generative AI Solutions certification.

While the course provides technical training aligned with certification objectives, the certification exam is not bundled and must be registered separately.

To maximise success, participants are strongly encouraged to complement the course with additional self-study, revision of course materials, and dedicated practice before attempting the exam.

Module 1: Experiment with Azure Machine Learning

  • Explore Azure Machine Learning workspace
  • Track experiments with MLflow
  • Use Automated Machine Learning (AutoML)
  • Apply Responsible AI dashboard

Module 2: Perform hyperparameter tuning with Azure Machine Learning

  • Configure sweep jobs
  • Optimize model performance
  • Compare experiment results

Module 3: Run pipelines in Azure Machine Learning

  • Create machine learning pipelines
  • Automate training workflows
  • Manage pipeline execution

Module 4: Trigger Azure Machine Learning jobs with GitHub Actions

  • Integrate Azure ML with GitHub
  • Automate training and deployment workflows
  • Configure CI/CD processes

Module 5: Trigger GitHub Actions with feature-based development

  • Branching strategies
  • Pull requests
  • Automated testing and validation

Module 6: Work with environments in GitHub Actions

  • Environment management
  • Secrets and configuration handling
  • Deployment governance

Module 7: Deploy a model with GitHub Actions

  • Model packaging
  • Endpoint deployment
  • Production deployment automation

Module 8: Plan and prepare a GenAIOps solution

  • GenAIOps concepts
  • AI application lifecycle
  • Environment planning
  • Governance considerations

Module 9: Manage prompts for agents in Microsoft Foundry with GitHub

  • Prompt version control
  • Prompt repositories
  • Agent development lifecycle

Module 10: Evaluate and optimize AI agents through structured experiments

  • AI agent evaluation techniques
  • Experiment tracking
  • Performance optimization

Module 11: Automate AI evaluations with Microsoft Foundry and GitHub Actions

  • Evaluation pipelines
  • Continuous testing
  • Automated quality checks

Module 12: Monitor your generative AI application

  • Application monitoring
  • Metrics collection
  • Observability practices

Module 13: Analyze and debug your generative AI app with tracing

  • Tracing AI workflows
  • Root cause analysis
  • Prompt and response debugging
*Important Note : Fees are subject to Singapore's prevailing Goods and Services Tax (GST).
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