AI-300 Bootcamp: MLOps Engineer Associate in 5 Days

AI-300 MLOps Engineer Bootcamp Overview

This MLOps engineer bootcamp is an accelerated, instructor-led path to Microsoft Certified: Machine Learning Operations Engineer Associate, earned by passing Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions. Over five hands-on days you cover both halves of the modern AI operations role: classical MLOps in Azure Machine Learning, and GenAIOps in Microsoft Foundry. The week runs from workspace and infrastructure setup through model training, registration and deployment, then into foundation model deployment, prompt versioning, evaluation and observability, and finishes on RAG optimization and advanced fine-tuning. You will sit the AI-300 exam on site before you leave.

AI-300 is the current Microsoft credential for this role. The previous data science certification built on DP-100 is retired, and its scope has moved on: where DP-100 concentrated on training models, AI-300 is about operating them in production, and it adds the generative AI operations work that did not exist in the older exam.

Who This AI-300 Course Is For

This AI operations training is built for machine learning engineers, data scientists moving into production work, MLOps and DevOps engineers, cloud engineers and software engineers who are responsible for AI systems after the model is built. Microsoft describes the audience as having subject matter expertise in setting up infrastructure for both MLOps and GenAIOps on Azure, together referred to as AI operations (AIOps). In the role you work alongside data scientists, DevOps teams and stakeholders to deliver scalable AI solutions with automation and monitoring.

What You'll Learn in the AI-300 Training

The AI-300 course follows the five skill areas Microsoft measures on the exam:

• Design and implement an MLOps infrastructure (15-20%): creating and managing Azure Machine Learning workspaces, datastores, compute targets and identity and access management; managing data assets, environments, components and cross-workspace sharing through registries; and implementing infrastructure as code with GitHub integration, Bicep and Azure CLI deployments, GitHub Actions provisioning workflows, network restrictions and Git source control.
• Implement machine learning model lifecycle and operations (25-30%): orchestrating training with MLflow experiment tracking, automated machine learning, hyperparameter tuning, distributed training and training pipelines; model registration and versioning including MLflow models, feature retrieval specifications and responsible AI evaluation; deployment to real-time and batch endpoints with progressive rollout and safe rollback; and production monitoring for data drift, performance thresholds and retraining triggers.
• Design and implement a GenAIOps infrastructure (20-25%): creating and configuring Microsoft Foundry resources and project environments; managed identities, role-based access control and private networking; deploying foundation models through serverless API endpoints and managed compute, selecting models per use case, versioning them and configuring provisioned throughput units for high-volume workloads; and managing prompts with variants, performance comparison and Git-based version control.
• Implement generative AI quality assurance and observability (10-15%): building test datasets and data mapping for evaluation, implementing AI quality metrics including groundedness, relevance, coherence and fluency, configuring risk and safety evaluations for harmful content, and automating evaluation workflows; plus continuous monitoring in Foundry covering latency, throughput, token consumption and cost, with logging, tracing and debugging for production troubleshooting.
• Optimize generative AI systems and model performance (10-15%): tuning retrieval-augmented generation through similarity thresholds, chunk sizes and retrieval strategies; selecting and fine-tuning embedding models for domain-specific accuracy; combining semantic and keyword-based hybrid search; evaluating RAG performance with relevance metrics and A/B testing; and advanced fine-tuning including synthetic data, monitoring and managing a fine-tuned model through to production.

Why Choose This MLOps Engineer Certification Camp

Get certified on one of Microsoft's newest AI credentials while the certified population is still small. This all-inclusive camp is led by Microsoft Certified Trainers as an authorized Microsoft Solutions Partner, using the Microsoft Official Course for AI-300 with official hands-on labs, and the exam administered while you attend.

• 5-day accelerated format: the full MLOps and GenAIOps scope in one week.
• 95% first-attempt pass rate, with a retake voucher if you need it.
• Microsoft Official Exam Voucher for AI-300 included, with no hidden exam fees.
• Microsoft official hands-on labs and vetted practice exams, plus 90 days of extended lab access after the camp.
• Onsite and online Pearson VUE testing, so you certify without booking a separate test center.
• Taught by Microsoft Certified Trainers, live online or in person in Sarasota, Florida.
• Classes run as scheduled, with no cancellations, so you can book travel with confidence.