Exam AI-300 Operationalizing Machine Learning and Generative AI Solutions (Video)

This course will help you prepare for the AI-300 Exam: Operationalizing Machine Learning and Generative AI Solutions.

This course follows Microsofts study guide for the AI-300 exam. Each video in the course covers a topic outlined in the exam.

A successful candidate for this exam and in the field of Machine Learning operations (MLOps) or Generative AI ops (GenAIOps) needs to understand the MLOps infrastructure, workspace infrastructure, automation, how to manage models, and configure agents. These core components and more are covered in this course.

Skill Level:

• Intermediate

Learn How To:

• Design and implement an MLOps infrastructure
• Implement machine learning model lifecycle and operations
• Design and implement a GenAIOps infrastructure
• Implement generative AI quality assurance and observability
• Optimize generative AI systems and model performance

Course requirement:

To be successful in this course, you should have subject matter expertise in setting up infrastructure for machine learning operations (MLOps) and generative AI operations (GenAIOps) solutions on Azure.

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video Training at  http://www.informit.com/video.

 Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Table of Contents

Course Introduction

Module 1: Design and implement an MLOps infrastructure

Lesson 1: Create and manage resources in a Machine Learning workspace

Lesson 2: Create and manage assets in a Machine Learning workspace

Lesson 3: Implement IaC for Machine Learning

Module 2: Implement machine learning model lifecycle and operations

Lesson 4: Orchestrate model training

Lesson 5: Implement model registration and versioning

Lesson 6: Deploy machine learning models for production environments

Lesson 7: Monitor and maintain machine learning models in production

Module 3: Design and implement a GenAIOps infrastructure

Lesson 8: Implement Foundry environments and platform configuration

Lesson 9: Deploy and manage foundation models for production workloads

Lesson 10: Implement prompt versioning and management with source control

Module 4: Implement generative AI quality assurance and observability

Lesson 11: Configure evaluation and validation for generative AI applications and agents

Lesson 12: Implement observability for generative AI applications and agents

Module 5: Optimize generative AI systems and model performance

Lesson 13: Optimize retrieval-augmented generation (RAG) performance and accuracy

Lesson 14: Implement advanced fine-tuning and model customization

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