AI
Operations
An AI service is live, but its owners cannot tell whether a bad output is a model issue, a data issue, or an operating issue. Build the monitoring, incident response, and ownership model that makes the service understandable and supportable.
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AWS· Solutions ArchitectAWS· Solutions ArchitectAbout this lab
Reliable AI strategy and operations depend on the systems around the model.
AI strategy and operations is the practice of aligning AI-enabled workflows and services to business goals, then turning that direction into digital transformation roadmaps, operating models, and reliable delivery. It brings together monitoring, incidents, cost, risk, vendor decisions, delivery governance, and the people who need to intervene when systems behave unexpectedly.
This lab focuses on operating decisions rather than model research or application architecture alone. You learn to make failures visible, define controls and service expectations, assess operational risk, and turn evidence into practical decisions for teams and stakeholders.
AI workflows have operational constraints: APIs fail, inputs vary, costs need monitoring, and people need a clear path to intervene. The useful work is in designing for those realities.
Your first brief starts with an AI service incident whose symptoms are visible but whose cause is unclear. You separate model, data, cost, and process signals, then produce the incident record, operating controls, and ownership decisions the team needs next.
The curriculum
Run AI systems that keep working
Move from operational foundations to reliable AI systems. Each phase turns a decision into evidence you can explain, test, and improve. Preview the opening phase, then unlock the complete lab when you purchase access.
Self-paced: typical completion varies by lab and starting point. See the study commitment below for the expected range.
Possible directions
Where this can lead
The lab helps you build evidence for AI strategy and operations roles. It does not guarantee a job.
Example employers for these skills
AWSExamples of organisations where related skills may be relevant. No employer endorsement is implied.
Skills you'll gain
Real tools, real skills.
You learn by doing, not watching. Every skill comes from completing practical module work and business challenges with the tools employers use.
Core skills
Tools you'll use
Salary ranges
What these roles actually pay.
Based on Glassdoor, LinkedIn, and Indeed data for 2025-2026. Ranges vary by location, experience, and company size.
AI Operations Specialist
UK
£55k-£90k
US
$85k-$140k
LLMOps Engineer
UK
£70k-£110k
US
$110k-$170k
AI Platform Manager
UK
£75k-£120k
US
$120k-$180k
Who this lab is for
You do not need to be a senior engineer. You need curiosity about how systems connect and a willingness to work through operational tradeoffs.
Operations and IT professionals
Learn to map systems, automate routine work, and assess where AI can help operational teams.
Developers and technical builders
Extend your technical foundation into prompt workflows, agent systems, evaluation, and operational reliability.
Analysts and career transitioners
Build a practical foundation in APIs, automation, and AI strategy and operations through structured projects.
Roles this work can support
AWS· Solutions ArchitectAWS· Solutions ArchitectHow our learners build their portfolios
Whether starting out, changing careers, or moving up.
Amara K.
Operations Lead
Priya M.
Product Manager
Daniel F.
Systems Analyst
Marcus W.
Technical PM
Nadia H.
Business Analyst
James R.
Technical Lead
Fatima A.
Solutions Architect
Chen L.
Founding Engineer
Samuel T.
AI Ops Specialist
Lisa K.
Platform Engineer
Grace N.
Compliance Manager
Hassan M.
Risk Analyst
What's included
Everything you need to finish.
Vera feedback on every submission
AI-powered review with specific, actionable notes
Up to 5 Vera reviews per submission
Use focused feedback to strengthen your work
Module tasks and business challenges
Monitoring plans, incident runbooks, cost analyses, and delivery decisions
One verifiable certificate per paid lab
Issued after the business-challenge defense passes
Lifetime access
Revisit materials and projects anytime
Level
Some technical familiarity helpful
Duration
11–13 weeks (5 hours per week)
Who this is for
- DevOps and platform engineers
- IT teams managing AI systems
- Analysts monitoring AI performance
Free to preview. Purchase the lab for complete access and more business challenges.
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