AI

AWS AI Cloud Engineer

Structured training in building and deploying AI/ML solutions on AWS — from model foundations through MLOps and production monitoring.

50
hours of live training
10 weeks
at one hour a day, weekdays
4
modules
24
topics covered

Before you start

Some technical background helpful. If you are not sure whether that describes you, the consultation exists to answer exactly that — and to tell you if a different programme is the better start.

Price

United Kingdom
£750
Canada
$1,750

Tax included — the price shown is the price you pay. This covers the training stage. The bootcamp and placement stages are priced separately.

Curriculum

A module-by-module breakdown

Everything covered in the program, in the order you'll cover it.

01AWS Foundations for AI Workloads

12 hours

The platform underneath the models

  • IAM roles & least privilege for ML
  • S3 data lakes & lifecycle policies
  • VPC design for private inference
  • EC2 GPU instances & Spot strategy
  • Cost monitoring & budgets
  • CloudWatch logging

02Amazon Bedrock & Generative AI

15 hours

Build on managed foundation models

  • Bedrock model selection & invocation
  • Knowledge Bases for RAG
  • Vector search with OpenSearch Serverless
  • Guardrails for content filtering
  • Bedrock Agents & tool use
  • Prompt management & versioning

03SageMaker & Custom Models

8 hours

When a managed model is not enough

  • SageMaker Studio & notebooks
  • Training jobs & hyperparameter tuning
  • Model registry & endpoints
  • Real-time vs batch inference
  • SageMaker Pipelines
  • Feature Store

04AI Services & Production

15 hours

The rest of the AWS AI surface, in production shape

  • Comprehend for NLP
  • Textract for document extraction
  • Rekognition for vision
  • Transcribe & Polly for speech
  • CI/CD for ML with CodePipeline
  • Monitoring, drift & a capstone deployment

Projects

Five projects, in AWS AI Cloud Engineer

The bootcamp runs the same five stages whatever you enrolled in, so the portfolio you finish with is deep in one technology rather than shallow across several. Each is scoped, designed, built and reviewed.

  1. 01 · Foundations

    A first build that puts the core concepts to work — environments configured, the basic workflows exercised, the fundamentals proven rather than assumed.

  2. 02 · Multiple moving parts

    A larger solution with several components and an integration or two, applying the practices that are conventional in your stack rather than improvising.

  3. 03 · Architecture that scales

    A design-led build: scalability, monitoring and the production-grade patterns your field actually uses, decided before they are implemented.

  4. 04 · An enterprise scenario

    A realistic situation with services that depend on each other, security to harden and performance to tune — the kind of work that is waiting on the other side of the offer.

  5. 05 · Capstone

    End to end, everything together: planned, designed, built, deployed and documented well enough to sit at the top of your GitHub profile.

What's included

Everything in the programme

The same seven things in every programme on the site — the subject is what changes.

  • Live training

    Taught live by a practitioner, not pre-recorded video you work through alone.

  • 50 hours

    Per programme as standard — 60 for IT Support Analyst, 70 for the AI Consultant and cloud engineering tracks.

  • Projects

    Five briefs in your own stack, each scoped, designed, built and reviewed the way a delivery team would.

  • Mentoring

    One mentor who stays with you for the whole programme, rather than whoever is free that week.

  • Resume & LinkedIn

    Both rewritten around the work you actually built, and positioned for the roles you are targeting.

  • Interview prep

    Mock interviews on the technical and the competency side, including defending the projects you built.

  • Placement support

    Your profile put in front of employers, and the search continues until you are hired.

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