AI-300: Operationalize machine learning and generative AI solutions

This course focuses on how to operationalize machine learning (MLOps) and generative AI (GenAIOps) solutions in Azure. You will learn how to build secure and scalable AI infrastructure, manage the full lifecycle of machine learning models, and deploy and optimize generative AI applications.

The course covers how to automate workflows, implement continuous integration and delivery, and apply infrastructure as code using tools such as GitHub Actions, Azure CLI, and Bicep. It also emphasizes monitoring, observability, and collaboration across data science and DevOps teams to deliver reliable, production-ready AI systems.

After completing the course, you will be able to move AI solutions from experimentation to production and manage them effectively in real-world environments.

Course Objectives

After completing this course, participants will be able to:

  • Design and implement MLOps solutions on Azure
  • Operationalize machine learning models using Azure Machine Learning
  • Implement GenAIOps workflows for generative AI applications
  • Automate deployment using CI/CD pipelines
  • Apply infrastructure as code for AI environments
  • Monitor, evaluate, and optimize AI solutions
  • Collaborate with DevOps and data science teams

Target audience

Data scientists, machine learning engineers, and DevOps professionals

This course is intended for professionals who want to design and operate production-grade AI solutions in Azure.

Prerequisites

Participants should have:

  • Experience with Python programming
  • Basic understanding of machine learning concepts
  • Familiarity with DevOps practices such as version control and CI/CD

MLOps fundamentals
Implement and manage the lifecycle of machine learning models using Azure Machine Learning

Model training and experimentation
Run experiments, tune models, and manage training workflows

CI/CD for AI
Automate deployment pipelines using GitHub Actions and Azure tools

Infrastructure as code
Provision and manage AI environments using Bicep and Azure CLI

Generative AI operations
Deploy, evaluate, and optimize generative AI applications and agents

Monitoring and observability
Track performance, debug issues, and improve reliability of AI systems

Collaboration and governance
Work across teams to deliver scalable and production-ready AI solutions

Practical information

Duration: 4 day
Price: 26 500 NOK
Language: English
Format: Classroom or virtual training, open or company-specific

FAQ

Hvordan gjennomføres kurset?
Kurset kan gjennomføres som et åpent kurs eller som bedriftsinternt kurs. Du kan delta enten fysisk i klasserom eller virtuelt.

Hvem passer kurset for?
Kurset passer for data scientists, maskinlæringsingeniører og DevOps-ressurser som jobber med AI-løsninger i produksjon.

Hva lærer jeg i løpet av kurset?
Du lærer hvordan du operasjonaliserer maskinlæring og generativ AI, og hvordan du bygger stabile og skalerbare løsninger i Azure.

Er kurset praktisk rettet?
Ja. Kurset inkluderer praktiske øvelser hvor du jobber med reelle scenarier innen MLOps og GenAIOps.

Hvilke temaer dekkes i kurset?
Kurset dekker blant annet MLOps, generativ AI, CI/CD, automatisering, modellutrulling og overvåking av AI-løsninger.

Får jeg sertifisering etter kurset?
Kurset er relevant for sertifisering innen Machine Learning Operations (MLOps), men eksamen må eventuelt tas separat.

Hvilke forkunnskaper anbefales?
Det anbefales erfaring med Python, grunnleggende maskinlæring og kjennskap til DevOps-prinsipper.

Hva gjør dette kurset unikt?
Kurset gir en helhetlig tilnærming til hvordan AI-løsninger faktisk settes i produksjon og driftes over tid, med fokus på både maskinlæring og generativ AI.

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