Operationalize machine learning and generative AI solutions

Overview

This course prepares learners to operationalise machine learning and generative ai solutions using microsoft azure, aligned to the requirements of the ai-300 exam. It focuses on building robust, scalable, and secure ai systems in production environments, combining machine learning operations and generative ai operations into a unified aiops approach.

Learners will gain the practical skills needed to deploy, monitor, govern, and optimise both traditional machine learning models and generative ai applications. The course emphasises real-world operational workflows, enabling organisations to transition from experimentation to enterprise-scale ai delivery with confidence.

Prerequisites

Learners should have:

• experience with python programming
• working knowledge of azure machine learning
• experience deploying and maintaining machine learning models
• familiarity with generative ai development concepts and tools such as microsoft foundry
• understanding of devops practices including source control and ci/cd
• experience with tools such as github actions and command-line interfaces

Tareget audience

This course is designed for professionals responsible for deploying and managing ai systems in production environments, including:

• ai engineers
• machine learning engineers
• data scientists with operational responsibilities
• devops and platform engineers supporting ai workloads

Objectives

By the end of this course, learners will be able to:

• design and implement mlops and genaiops workflows on azure
• deploy and manage machine learning models in production environments
• operationalise generative ai applications and agents
• implement monitoring, observability, and governance for ai systems
• optimise performance, cost, and reliability of ai solutions
• apply security and compliance best practices across the ai lifecycle

Outline

Module 1: introduction to ai operations on azure

This module introduces the principles of aiops, combining mlops and genaiops into a unified operational framework.

• understanding aiops concepts and lifecycle
• differences between experimentation and production ai
• overview of azure ai services and architecture
• introduction to azure machine learning and microsoft foundry
• defining operational requirements for ai solutions

Module 2: setting up infrastructure for ai workloads

This module focuses on preparing the infrastructure required to support scalable ai operations.

• provisioning azure machine learning workspaces
• configuring compute resources and environments
• managing data storage and access
• implementing identity and access management
• setting up networking and security controls

Module 3: implementing mlops workflows

This module covers the deployment and lifecycle management of machine learning models.

• versioning datasets, models, and code
• building automated training pipelines
• deploying models to endpoints
• implementing ci/cd for machine learning
• managing model lifecycle and updates

Module 4: operationalising generative ai solutions

This module focuses on deploying and managing generative ai applications and agents.

• deploying generative ai models using microsoft foundry
• building and managing ai agents
• integrating generative ai into applications
• managing prompts, embeddings, and vector stores
• evaluating generative ai outputs and performance

Module 5: monitoring and observability

This module explores how to monitor ai systems in production to ensure reliability and performance.

• implementing logging and telemetry
• monitoring model performance and drift
• tracking generative ai usage and outputs
• setting up alerts and dashboards
• troubleshooting production issues

Module 6: governance and responsible ai

This module focuses on governance frameworks and responsible ai practices.

• implementing responsible ai principles
• managing compliance and regulatory requirements
• auditing ai systems and decision-making processes
• managing data privacy and security
• applying governance policies across ai workloads

Module 7: optimisation and scaling

This module teaches how to improve performance, cost-efficiency, and scalability of ai systems.

• optimising model performance and latency
• scaling infrastructure for high-demand workloads
• cost management strategies for ai services
• optimising generative ai usage and responses
• implementing caching and efficiency techniques

Module 8: end-to-end ai solution lifecycle

This module consolidates learning by examining the full lifecycle of ai solutions in production.

• designing end-to-end ai pipelines
• integrating mlops and genaiops workflows
• managing continuous improvement cycles
• case study: productionising an ai solution on azure
• preparing for the ai-300 exam

Exams and assessment.

This course aligns directly with the requirements of exam ai-300: operationalizing machine learning and generative ai solution but the exam is not included in the course.

Hands-on learning

This course includes:

• Guided demonstrations of Azure AI services in action
• Interactive exercises using Microsoft Learn modules
• Practical examples of building simple AI applications and agents
• Instructor-led walkthroughs to support real-world understanding