---- Microsoft Azure AI · Instructor-Led Workshop
Build AI
that Thinks
on Azure.
that Thinks
on Azure.
From API calls to production-grade AI systems — master Azure OpenAI, Cognitive Services, Machine Learning, and Responsible AI across three progressive workshop levels. Prep for AI-900 and AI-102 certifications.
AI-900 Certified
AI-102 Certified
3×Workshop Levels
35+Hands-on Labs
2×Azure Exams Covered
94%AI-102 Pass Rate
---- Three Levels
Choose Your
Azure AI Level.
Azure AI Level.
Level 01 — Beginner
Azure AI Fundamentals
Get hands-on with Azure Cognitive Services, language models, and the AI-900 exam blueprint. Perfect for professionals entering the AI space with no prior ML background.
Key Modules
Introduction to Azure AI & Cognitive Services
Azure OpenAI — chat, completion & embeddings
Computer Vision & Custom Vision basics
Natural Language Processing with Azure AI
Responsible AI principles & Azure guardrails
AI-900 PrepAI-102 Foundation
GBP
£595
per person
/
USD
$749
per person
⭐ Most Popular
Level 02 — Intermediate
Azure AI Engineer
Build, deploy, and monitor production-ready AI solutions. Deep-dive into Azure OpenAI, RAG pipelines, vector search with Azure AI Search, and AI-102 certification prep across four intensive days.
Key Modules
Azure OpenAI — fine-tuning & function calling
RAG architecture with Azure AI Search & embeddings
Prompt engineering & LLM evaluation frameworks
Azure ML — pipelines, model registry, deployment
AI-102 mock exams & scenario walkthroughs
AI-102 CoreAzure OpenAI
GBP
£1,195
per person
/
USD
$1,499
per person
Level 03 — Advanced
Enterprise AI & MLOps
Design resilient, governed, multi-model AI architectures at enterprise scale. Covers Azure AI Foundry, MLOps pipelines, monitoring, cost governance, and advanced agentic AI patterns.
Key Modules
Azure AI Foundry — model catalogue & hub setup
Agentic AI with Azure AI Agents & Semantic Kernel
MLOps — CI/CD for models, drift detection
AI governance, cost controls & content filtering
Multi-modal AI — vision, speech, document intelligence
AI-102 AdvancedEnterprise Track
GBP
£1,795
per person
/
USD
$2,249
per person
Level 01 — Beginner
Azure AI Fundamentals
Get hands-on with Azure Cognitive Services, language models, and the AI-900 exam blueprint. Perfect for professionals entering the AI space with no prior ML background.
Key Modules
Introduction to Azure AI & Cognitive Services
Azure OpenAI — chat, completion & embeddings
Computer Vision & Custom Vision basics
Natural Language Processing with Azure AI
Responsible AI principles & Azure guardrails
AI-900 PrepAI-102 Foundation
GBP
£595
per person
/
USD
$749
per person
⭐ Most Popular
Azure AI Engineer
Build, deploy, and monitor production-ready AI solutions. Deep-dive into Azure OpenAI, RAG pipelines, vector search with Azure AI Search, and AI-102 certification prep across four intensive days.
Key Modules
Azure OpenAI — fine-tuning & function calling
RAG architecture with Azure AI Search & embeddings
Prompt engineering & LLM evaluation frameworks
Azure ML — pipelines, model registry, deployment
AI-102 mock exams & scenario walkthroughs
AI-102 CoreAzure OpenAI
GBP
£1,195
per person
/
USD
$1,499
per person
Level 03 — Advanced
Enterprise AI & MLOps
Design resilient, governed, multi-model AI architectures at enterprise scale. Covers Azure AI Foundry, MLOps pipelines, monitoring, cost governance, and advanced agentic AI patterns.
Key Modules
Azure AI Foundry — model catalogue & hub setup
Agentic AI with Azure AI Agents & Semantic Kernel
MLOps — CI/CD for models, drift detection
AI governance, cost controls & content filtering
Multi-modal AI — vision, speech, document intelligence
AI-102 AdvancedEnterprise Track
GBP
£1,795
per person
/
USD
$2,249
per person
---- What You'll Learn
Full Azure AI
Curriculum.
Curriculum.
Azure AI Foundations
📋 Fundamentals Workshop
Azure AI services overview — what is available and when to use each
Azure OpenAI Service — models, deployments, and API basics
Chat completions, text generation, and embedding use cases
Azure Computer Vision — image classification and object detection
Azure Speech & Language Services — transcription, translation, NER
Responsible AI principles — fairness, transparency, and content safety
Azure OpenAI & Prompt Engineering
📋 AI-102 Foundation
Prompt engineering patterns — zero-shot, few-shot, chain-of-thought
Function calling & tool use — connecting models to external systems
Parameter tuning — temperature, top-p, max tokens, and stop sequences
Tokenisation, context windows, and cost optimisation strategies
PromptFlow — building and evaluating prompt pipelines systematically
Content filtering & jailbreak detection with Azure AI Content Safety
RAG Pipelines & Vector Search
📋 AI-102 Core
RAG architecture — retrieval-augmented generation from first principles
Embeddings — generating, storing, and querying vector representations
Azure AI Search — indexing, vector fields, and semantic re-ranking
Document chunking strategies — fixed, semantic, and hierarchical
Evaluating RAG quality — groundedness, relevance, and coherence metrics
Hybrid search — combining keyword and vector retrieval for best results
Azure Machine Learning
📋 All Levels
AML workspace — compute clusters, environments, and datastores
Training pipelines — components, inputs/outputs, and job scheduling
AutoML — classification, regression, time series with no-code ML
Experiment tracking — MLflow metrics, parameters, and model registration
Managed online endpoints — real-time inference with auto-scaling
Responsible AI dashboard — error analysis, fairness, and explainability
MLOps & Model Governance
📋 AI-102 Advanced
CI/CD for ML — triggering retraining on data drift with Azure DevOps
Model monitoring — data drift, concept drift, and prediction quality
Model registry — versioning, staging, and promotion workflows
Cost governance — Azure AI budgets, quotas, and usage alerts
Private endpoints, VNET integration, and managed identity for AI
Deployment strategies — blue/green, canary, and shadow deployments
Agentic AI & Enterprise Patterns
📋 Enterprise Track
Azure AI Agents Service — planning, tool use, and multi-step reasoning
Semantic Kernel — plugins, memory, and agent orchestration in .NET/Python
Azure AI Foundry — model catalogue, hub setup, and project management
Multi-modal AI — combining vision, speech, and language in one pipeline
Enterprise architecture patterns — multi-tenant, multi-region AI at scale
AI governance at scale — audit trails, sensitivity labels, and lineage
---- Who Attends
Built for Every
AI Professional.
AI Professional.
Software Developers
Integrate Azure OpenAI and Cognitive Services APIs into applications. Build intelligent features without needing a deep ML background.
Data Scientists
Move from experimentation to production. Use Azure ML pipelines, AutoML, and managed endpoints to ship models that actually run in the real world.
Solutions Architects
Design scalable, secure, cost-governed AI systems on Azure. Understand when to use managed services vs. custom models and how to stitch them together.
Leaders & PMs
Understand Azure AI capabilities and limitations. Make informed build-vs-buy decisions, govern AI projects effectively, and champion responsible AI inside your organisation.
---- Tools & Stack
Every Tool You'll
Work With.
Work With.
Every attendee uses a live Azure subscription throughout the workshop — real compute, real costs, real learning.
Azure OpenAI Service
GPT-4o, text-embedding-ada, DALL·E 3, and Whisper — all on Azure with enterprise security, private networking, and content filtering built in.
All levels
Azure AI Search
Build RAG pipelines combining vector search, hybrid ranking, and semantic re-ranking. The cornerstone of enterprise knowledge retrieval on Azure.
Intermediate+
Azure AI Foundry
The unified hub for model selection, evaluation, fine-tuning, and deployment. Explore the model catalogue from GPT to Phi to Mistral — all in one place.
Intermediate+
Azure Machine Learning
Design, train, register, and deploy custom ML models at scale. Covers Pipelines, AutoML, Responsible AI dashboard, and managed online endpoints.
Intermediate+
Azure Cognitive Services
Computer Vision, Document Intelligence, Language, Speech, and Translator — pre-built AI APIs that drop into any application with minimal code.
All levels
Semantic Kernel & LangChain
Orchestrate multi-step AI workflows, agents, and tool-use patterns. Build reliable agentic systems on top of Azure OpenAI with proper memory and planning.
Advanced track
Azure AI Content Safety
Detect harmful content, apply groundedness filtering, and configure jailbreak detection. Essential for any public-facing AI product on Azure.
All levels
PromptFlow & Evaluation
Build, test, and evaluate LLM pipelines with Azure PromptFlow. Track metrics like coherence, groundedness, and relevance across prompt variants.
Intermediate+
---- Why Azure AI
Why Learn
Azure AI Now?
Azure AI Now?
01
£10B Microsoft Azure AI Investment
Microsoft has committed over £10 billion to UK AI infrastructure and embedded OpenAI models natively across Azure. Enterprises buying Azure are buying Azure AI — certified engineer demand is surging.
02
AI-102 = Six-Figure Salary Territory
AI-102 certified engineers in the UK command £80k–£120k salaries. In the US market, Azure AI Engineers regularly clear $130k–$180k. Certification is one of the fastest ROI moves in tech right now.
03
Every Enterprise Is Building AI — Fast
From FTSE 100 banks to NHS digital teams, Azure is the enterprise AI platform of choice in the UK. Organisations are hiring now, and the skills gap is widening, not narrowing.
85%
of Fortune 500 of FTSE 100 companies are actively deploying or piloting Azure AI solutions — making this the most in-demand enterprise AI skill in the market.
85%
of Fortune 500 of FTSE 100 companies are actively deploying or piloting Azure AI solutions — making this the most in-demand enterprise AI skill in the market.
04
Responsible AI Is Now a Legal Requirement
With the EU AI Act in force and the UK AI Safety Institute active, understanding governance, explainability, and content safety is no longer optional — it’s a core engineering skill.
---- Learner Stories
What Our
Graduates Say.
Graduates Say.
“
The RAG pipeline lab was worth the entire course fee. I had been struggling to get consistent retrieval quality for weeks — in one afternoon I understood why my chunking strategy was wrong and fixed it on the spot.
“
Passed AI-102 first attempt, two weeks after the intermediate workshop. The instructor knew exactly which topics tripped people up in the exam and we drilled those scenarios repeatedly. Highly recommend.
“
We ran a corporate batch for 18 of our consultants. Within a quarter we had shipped three client-facing AI pilots, two of which went to production. The ROI on the training was clear within 90 days.
