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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
Delegates will learn how to
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
Frequently asked questions
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How do QA’s online courses work?
QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course.
Once you have purchased the Online course and have completed your registration, you will receive the necessary details to enable you to immediately access it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
When will I receive my joining instructions?
Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.
When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.