AI-300: Machine Learning Operations Engineer Associate
Run machine learning and generative AI in production on Azure: infrastructure as code, model lifecycle, GenAIOps, evaluation and monitoring.
About AI-300
AI-300 covers both MLOps for traditional models in Azure Machine Learning and GenAIOps for generative AI apps and agents in Microsoft Foundry. The certification name has changed since the exam was announced. Check the latest exam objectives on Microsoft Learn.
Course Overview
This is a Microsoft Standard Course conducted by Microsoft Certified Trainers (MCT) using the official Microsoft curriculum.
This course is led by a Microsoft Certified Trainer (MCT) and follows the official skills measured for Exam AI-300. It shows you how to take models and AI apps from experiment to reliable production.
You will set up repeatable infrastructure, automate training and deployment, manage prompts in source control, and evaluate, observe and optimise generative AI systems.
What You Will Learn:
- • Set up Azure Machine Learning workspaces, compute and assets
- • Automate infrastructure with Bicep and the Azure CLI
- • Orchestrate training and register model versions
- • Deploy models to production and monitor them
- • Configure Microsoft Foundry environments and deploy foundation models
- • Version prompts in source control with GitHub Actions
- • Evaluate and observe generative AI apps and agents
- • Improve RAG accuracy and decide when to fine-tune
Perfect For:
- • Data scientists moving models into production
- • Machine learning engineers
- • DevOps and platform engineers supporting AI
- • AI engineers running generative AI apps in production
- • Teams standardising how they ship AI
Training Details
Training Format:
- Live instructor-led training by a Microsoft Certified Trainer
- Online or on-site delivery
- Hands-on labs throughout the course
- Small Cohort · Personalised Attention
What's Included:
- Official Microsoft Courseware & Slides
- Microsoft Digital Achievement Badge
- Exam Preparation Support & Tips
- WhatsApp Community & Post-training Support
Prerequisites & Recommendations
Technical Prerequisites:
- • Python and data science fundamentals
- • Entry-level DevOps: Git, GitHub Actions and command-line tools
- • Familiarity with Azure Machine Learning or Microsoft Foundry
- • Basic infrastructure-as-code concepts are helpful
Course Curriculum (4 days Hands-on Training)
Module 1: MLOps Infrastructure on Azure
- Create and manage workspace resources and compute
- Manage data, environments and components as assets
- Define infrastructure as code with Bicep and the Azure CLI
- Separate development, test and production environments
Module 2: Model Training and Lifecycle
- Orchestrate training with pipelines and jobs
- Track experiments and compare runs
- Register and version models
- Promote models through environments
Module 3: Deploying and Maintaining Models in Production
- Deploy models to online and batch endpoints
- Roll out new versions safely
- Monitor performance and data drift
- Retrain and maintain models over time
Module 4: GenAIOps Infrastructure with Microsoft Foundry
- Configure Foundry environments and platform settings
- Deploy and manage foundation models for production
- Manage prompts with versioning and source control
- Automate releases with GitHub Actions
Module 5: Quality Assurance and Observability for Generative AI
- Set up evaluations for apps and agents
- Validate quality, safety and groundedness
- Add tracing and observability
- Build alerting and feedback loops
Module 6: Optimising Generative AI Systems
- Improve retrieval accuracy and performance in RAG
- Tune chunking, indexing and ranking
- Decide when to fine-tune or customise a model
- Balance quality, speed and cost
AI Career Path & Related Certifications
Build your AI expertise progressively with these Microsoft certifications:
Frequently Asked Questions
What does AI-300 cover?
Running AI in production: MLOps for traditional machine learning in Azure Machine Learning, and GenAIOps for generative AI apps and agents in Microsoft Foundry, including evaluation, monitoring and optimisation.
Do I need AI-103 first?
It is not required. AI-103 helps with the generative AI topics, and many learners take AI-901 first if they are new to AI on Azure.
Is this course for data scientists or DevOps engineers?
Both. It suits data scientists who need to ship models, and DevOps or platform engineers who support AI workloads.
Does AI-300 count towards AB-100?
Microsoft lists it among the Associate credentials that can qualify you for the AB-100 Agentic AI Business Solutions Architect Expert certification. Check the latest exam objectives on Microsoft Learn.
How is the training delivered?
Live and instructor-led by a Microsoft Certified Trainer (MCT), online or on-site, with hands-on labs throughout. You can ask questions in real time and get post-training support on WhatsApp.
When is the next batch?
Contact us on WhatsApp for upcoming dates. We share current batch options, group pricing and on-site availability for your team.
Quick Information
AI-300 Exam Details:
• Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions
• Passing score: 700
• Certification: Microsoft Certified: Machine Learning Operations Engineer Associate
• Check the latest exam objectives on Microsoft Learn.
Pricing
AED 6,955
AED · per participant
Group & early-bird discounts available — ask on WhatsApp
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