AI-300

MLOps Engineer Associate (AI-300)

Design and implement machine learning operations and generative AI operations on Azure. Master ML model lifecycle management deployment automation and GenAI observability.

75 lessons
15 hours
5 modules
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Course Content

5 modules · 75 lessons

Module 1: Design and Implement MLOps Infrastructure

Design and implement MLOps infrastructure for machine learning workloads. (15-20% of exam)

MLOps Environment Setup

Set up MLOps environments on Azure.

CI/CD for ML

Implement CI/CD pipelines for ML.

Infrastructure Security

Secure MLOps infrastructure.

Module 2: Manage ML Model Lifecycle

Manage the complete machine learning model lifecycle from training to retirement. (25-30% of exam)

Model Training and Experimentation

Implement model training and experimentation.

Model Registration and Versioning

Implement model registration and versioning.

Model Deployment

Deploy ML models to production.

Model Monitoring

Monitor ML models in production.

Responsible AI in MLOps

Implement responsible AI practices in MLOps.

Module 3: Design and Implement GenAIOps Infrastructure

Design and implement generative AI operations infrastructure on Azure. (20-25% of exam)

Azure AI Foundry Setup

Set up Azure AI Foundry for GenAIOps.

GenAI Deployment Strategies

Implement GenAI deployment strategies.

GenAI Security and Compliance

Implement security for GenAI solutions.

Module 4: Implement GenAI QA and Observability

Implement quality assurance and observability for generative AI solutions. (10-15% of exam)

GenAI Quality Assurance

Implement QA for GenAI solutions.

GenAI Observability

Implement observability for GenAI.

Advanced Evaluation Techniques

Advanced evaluation for GenAI.

Module 5: Optimize GenAI Systems

Optimize generative AI systems for performance cost and quality. (10-15% of exam)

Performance Optimization

Optimize GenAI system performance.

Cost Optimization

Optimize GenAI costs.

Quality Optimization

Optimize GenAI output quality.

Scalability Patterns

Scale GenAI systems effectively.

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