Overview
*AI-300 This course replaces DP-100 and has been updated to reflect the latest AI-300 MLOps and GenAIOps curriculum.
Learn how to operationalize machine learning and generative AI solutions on Azure at cloud scale. This course teaches you how to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions using Azure Machine Learning and Microsoft Foundry.
This course is designed for data scientists, machine learning engineers, and DevOps professionals who want to build, deploy, evaluate, monitor, and optimize production-ready AI solutions. It covers secure and scalable AI infrastructure, automation, continuous integration and delivery, infrastructure as code, and observability using tools such as GitHub Actions, Azure CLI, and Bicep.
What you’ll learn
Module 1: Design and implement an MLOps infrastructure:
- Create and manage Azure Machine Learning workspaces, datastores, compute targets, and identity and access management.
- Create and manage data assets, environments, components, and registries.
- Implement infrastructure as code for machine learning with GitHub integration, Bicep, Azure CLI, and GitHub Actions.
- Manage source control and secure access for machine learning projects.
Module 2: Implement machine learning model lifecycle and operations:
- Configure experiment tracking with MLflow.
- Use automated machine learning, notebooks, hyperparameter tuning, training scripts, and pipelines.
- Register and version models, evaluate them using responsible AI principles, and manage the model lifecycle.
- Deploy models as real-time or batch endpoints and implement progressive rollout and rollback strategies.
- Monitor deployed models for data drift, performance, and retraining triggers.
Module 3: Design and implement a GenAIOps infrastructure:
- Create and configure Foundry resources and project environments.
- Configure identity, access management, network security, and private networking.
- Deploy infrastructure using Bicep templates and Azure CLI.
- Deploy foundation models using serverless API endpoints and managed compute options.
- Manage model versioning, production deployment strategies, and prompt source control.
Module 4: Implement generative AI quality assurance and observability:
- Create test datasets and evaluation workflows for generative AI applications and agents.
- Implement quality metrics such as groundedness, relevance, coherence, and fluency.
- Configure risk and safety evaluations for harmful content detection.
- Monitor latency, throughput, response times, cost, token consumption, logging, tracing, and debugging.
Module 5: Optimize generative AI systems and model performance:
- Optimize RAG performance using retrieval strategies, similarity thresholds, and chunk sizes.
- Select and fine-tune embedding models for domain-specific use cases.
- Implement hybrid search approaches and evaluate RAG performance with A/B testing.
- Design advanced fine-tuning methods, manage synthetic data, and optimize fine-tuned models from development to production.
Accreditation
ITCE is a Microsoft accredited Solutions Partner for Data & AI.
Calendar
Frequently Asked Questions
Who should attend?
This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure. It is best suited for learners with Python experience, foundational machine learning knowledge, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools.
Are there any prerequisites for this course?
Learners should already have experience with Python, a basic understanding of machine learning concepts, and introductory knowledge of DevOps practices. Experience with Git, CI/CD workflows, and cloud resources in Azure will be helpful.
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