AI PRO Technical Implementation & Automation

AI has crossed from experiments into production: processes are being automated, decisions accelerated, whole workflows rebuilt around language models. The gap in most organisations is no longer whether to use AI — it is the engineer who can take a model beyond the demo: adapt it to the company’s data, ground it in the company’s knowledge, wire it into systems that act, and run all of it securely in production.

This advanced course builds that engineer over three instructor-led days. You work through the full technical stack of applied AI — fine-tuning, retrieval-augmented generation (RAG) and agent systems — implementing each one hands-on. You leave with working code and, more importantly, with the architectural judgement to choose between them: when a prompt is enough, when retrieval beats retraining, and when an agent is worth its complexity.

Who this course is for

• Software developers and engineers who want to build AI-powered features and services, not just call an API
• Data scientists and ML practitioners moving from notebooks and prototypes to production systems
• Solution architects and technical consultants designing AI workflows for teams or clients
• Technically minded professionals who have completed AI Foundation, or have equivalent grounding, and want to go deep on implementation

What you will learn

• Explain modern machine-learning and LLM architectures well enough to make sound model-selection and build-or-buy decisions
• Prepare, validate and govern the data that training and retrieval depend on — selection, cleaning and bias reduction
• Fine-tune existing models with parameter-efficient techniques and evaluate whether fine-tuning was the right call at all
• Build retrieval-augmented generation (RAG) pipelines with embeddings and vector databases, grounded in your own documents
• Design and orchestrate AI agents — tool use, API and MCP interfaces, multi-agent structures — with humans in the loop where it matters
• Secure LLM applications against the OWASP Top 10 for LLMs: prompt injection, data leakage and insecure output handling
• Take AI systems to production: deployment, monitoring, CI/CD for models, and the technical measures behind GDPR and AI Act conformity

Course outline

Day one — Foundations, models and adaptation

• Modern AI architectures: transformers, LLMs and embeddings — what an implementer actually needs to know
• Choosing models: open versus hosted, small versus large, cost and latency trade-offs
• Data for AI: selection, preparation, validation and bias reduction — hands-on
• Fine-tuning in practice: parameter-efficient methods, evaluation, and when not to fine-tune — hands-on

Day two — Retrieval and grounded systems

• RAG architectures: chunking, embeddings, vector databases and retrieval quality
• Hands-on: build a working RAG pipeline over real documents
• Reducing hallucination: grounding, citations and systematic evaluation of RAG output
• LLM security: the OWASP Top 10 for LLMs — prompt injection, data leakage, insecure output handling — attack and defence, hands-on

Day three — Agents, production and compliance

• AI agents: tool use, function calling, API and MCP interfaces — hands-on: build a working agent
• Multi-agent structures and orchestration — and when a single model is the better answer
• To production: deployment patterns, monitoring, and CI/CD for AI systems
• Technical compliance: anonymisation, filtering and hosting choices behind GDPR and AI Act conformity
• Closing project: an end-to-end system combining retrieval, an agent and guardrails; exam preparation

Prerequisites

Working knowledge of Python is required — the labs use it throughout. Familiarity with APIs and JSON is helpful. No prior machine-learning experience, advanced mathematics or deep-learning background is required: the course builds the theory it needs as it goes. AI Foundation, or an equivalent grounding in AI concepts, is recommended.

Exam and certification

The course prepares you for the ICO Artificial Intelligence PRO examination, awarded by ICO International Certification Organization GmbH — an accredited certification exam, not a certificate of attendance.

• 40 multiple-choice questions — one, several or all answer options may be correct
• 80 minutes, closed book
• Pass mark of 60%
• Taken online, at a time and place you choose
• Certificate valid for 36 months

The exam is available as an optional add-on to the course. Holders of both the FND and PRO certificates qualify for the ICO Artificial Intelligence Professional role certificate. Full details are published by the awarding body at ico-cert.org.

What is included

• Three days (24 hours) of live, instructor-led online training, delivered in English
• Small groups — never more than 22 participants
• Hands-on labs in every block, with complete course materials and sample exam questions
• A closing project you build yourself and keep
• Certificate of attendance; the ICO Artificial Intelligence PRO exam as an optional add-on