+44 (0) 2080 049141info@londonittraining.co.uk

LONDON IT TRAINING

Find your next course

Explore our training courses by subject, skill or certification.

All courses

Classroom · Live online · Training for teamsFind the right course for your next step.
  1. Home
  2. »
  3. Azure MLOps and GenAIOps: AI-300 Preparation
Instructor-led training
Azure MLOps and GenAIOps: AI-300 Preparation Training in London and Live Online

Azure MLOps and GenAIOps: AI-300 Preparation

Operationalise machine learning models and generative AI applications on Azure. Prepare for AI-300 with repeatable infrastructure, model lifecycle controls, quality assurance and production monitoring.

Request Course Information
Price:
Price on request

Contact us for current fees, dates and exam arrangements.

Advanced

Python and data science experience with Azure machine learning tools, version control and basic DevOps practices.

Contact us for duration to complete
Available online
& in class

Select your preferred method

Course overview
Course dates
  • In Classroom
  • Live Virtual
  • 1 - 1 Training
  • Corporate Training
  • Onsite Training
Introduction
Operationalise machine learning models and generative AI applications on Azure. Prepare for AI-300 with repeatable infrastructure, model lifecycle controls, quality assurance and production monitoring.


This instructor-led course combines clear explanations with guided exercises and practical scenarios. Work through examples relevant to the subject, review alternative approaches and practise checking your results rather than relying on a demonstration alone.
You will develop a structured approach to implementation, troubleshooting and documentation. The trainer can adapt examples to the agreed learning needs and available training environment. Confirm the course duration, software or lab requirements, delivery arrangements and fees with London IT Training before booking.
The course supports preparation for Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions. Training attendance does not award the credential. The awarding body sets separate examination, eligibility and experience requirements; confirm these and any exam fees before booking.
What the course covers
Flexible Ways to Learn

Study your way with flexible learning options designed to fit around your schedule.

Small Groups

With small groups of students, our instructors can work closely with each student

Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend or evening to suit your schedule.

Experienced Instructors

Our instructors follow a modified and personalised approach to engage students during class

Hi-Tech lab Facilities

Lab arrangements vary by course. Confirm access and equipment requirements before booking.

Prerequisites
What you’ll gain

Apply ml platform infrastructure in a practical course exercise.

Apply training and model lifecycle in a practical course exercise.

Apply inference deployment and maintenance in a practical course exercise.

Apply generative ai operating environments in a practical course exercise.

Develop skills for your next step
Relevant career pathways
Technical Practitioner:

Apply the course skills to relevant technical tasks.

Technical Consultant:

Assess requirements and recommend practical implementation choices.

Syllabus

Course context and practical foundations
Review the course objectives and relevant concepts. Set up a suitable training environment, identify a realistic example and agree how to validate the results.

Plan workspace resources, permissions, data assets and compute. Provision a repeatable environment with appropriate network boundaries.

Track experiments, compare metrics and version artefacts. Build evaluation and approval stages into model promotion.

Compare deployment patterns, validate endpoints and define rollout, rollback and model retirement procedures.

Configure model and application environments, control access and version prompts and infrastructure alongside code.

Build evaluation datasets, inspect traces and monitor application and model behaviour. Investigate regressions and safety findings.

Measure latency, throughput and resource consumption. Compare changes without sacrificing required quality and security.

Complete an integrated exercise using sample data or an isolated training environment. Test the result, document decisions and troubleshooting steps, and identify improvements and next learning objectives.

1
2
3
4
5
6
7

FAQs

Who is this course for?
Operationalise machine learning models and generative AI applications on Azure. Prepare for AI-300 with repeatable infrastructure, model lifecycle controls, quality assurance and production monitoring.

Python and data science experience with Azure machine learning tools, version control and basic DevOps practices.

ML platform infrastructure; Training and model lifecycle; Inference deployment and maintenance; Generative AI operating environments; Quality assurance and observability; Performance and cost optimisation.

No. Check the awarding body’s current exam, experience and eligibility requirements. Exam booking and fees must be confirmed separately.

Contact London IT Training to discuss classroom, live online and corporate delivery arrangements.

Contact the training team for current duration, available dates, fees and software or lab requirements. Do not assume that licences, materials or examination fees are included.

Need help choosing the right course?

Talk to one of our advisers about dates, delivery options and certification routes. We’ll call you back within 1 working day.

LONDON IT TRAINING

Let’s plan
your next step.

Tell us what you would like to learn.

  • Course guidance
  • Dates and delivery
  • Training for teams

Your details are used to respond to your enquiry. Privacy Policy