self-paced

Building with Microsoft AI 501

Build with Microsoft AI services, structured outputs, retrieval and production controls.

This page is the course itself. Commercial AI training for UK companies is owned by the AI training hub.

Delivered by AI Build

Learning platform: SAVI.education

Certificate issued by SAVI.education

Duration
8 hours
Price
£149
Level
Advanced

Who this course is for

Who it's for
People whose work sits in AI Tools for Business and who want a practical, workplace-focused course rather than a generic overview.
Prerequisites
Prior experience with workplace AI tools is recommended. Have an approved tool available so you can practise.
What you'll be able to do
  • Complete “AI Application Architecture” and apply it to a real task at work.
  • Complete “Model and Provider Selection” and apply it to a real task at work.
  • Complete “Embeddings and Semantic Search” and apply it to a real task at work.
  • Complete “Deployment, Monitoring and Evaluation” and apply it to a real task at work.
Duration & level
8 hours · Advanced
  • Self-paced lessons
  • Credential

Course syllabus

Lessons are grouped into clear steps with visual cues, so each course is easier to scan before you start.

Lessons unlock after purchase. Create your account with the same email you paid with — no course code is required — or choose “Already purchased?” above.

  1. Step 1 · Lesson

    AI Application Architecture

    Learn how to design robust AI application architectures that connect models, data, tools, users and governance controls. You will be able to choose an appropriate architectural pattern, define component responsibilities, and plan for reliability, security, evaluation and maintainability from the start.

  2. Step 2 · Lesson

    Model and Provider Selection

    Learn how to choose the right AI model and provider for a production application using practical criteria such as capability, latency, cost, data governance and integration fit. You will be able to design a model selection scorecard, run meaningful evaluations and decide when to use one model, multiple models or a provider abstraction layer.

  3. Step 3 · Lesson

    Embeddings and Semantic Search

    Learn how embeddings turn text and other content into searchable numerical representations, and how semantic search differs from traditional keyword search. You will be able to design an embedding pipeline, choose chunking and indexing strategies, and evaluate search quality for business applications.

  4. Step 4 · Lesson

    Deployment, Monitoring and Evaluation

    Learn how to take an AI application from prototype to production with clear deployment patterns, monitoring, evaluation and rollback plans. You will be able to define practical quality, cost, safety and reliability controls for RAG, agentic and API-based AI systems.