AI ENGINEER · FROM THE FUNDAMENTALS OF MACHINE LEARNING TO AI IN PRODUCTION →

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Technical programme · 9 modules · Applied learning

AI Engineer

AI Engineer

From the fundamentals of machine learning to AI in production

From the fundamentals of machine learning to AI in production

A comprehensive curriculum that trains AI engineers capable of building, evaluating, deploying, and governing machine learning and generative AI systems in real-world environments. It combines self-paced e-learning for theory with practical, instructor-led workshops across nine modules, ranging from fundamentals to production release, ethics, and security.

A comprehensive curriculum that trains AI engineers capable of building, evaluating, deploying, and governing machine learning and generative AI systems in real-world environments. It combines self-paced e-learning for theory with practical, instructor-led workshops across nine modules, ranging from fundamentals to production release, ethics, and security.

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The programme

An end-to-end AI engineering programme

The AI Engineer covers the entire AI lifecycle: preparing data, building and evaluating models, putting them into production, and keeping them secure and responsible. Each module combines self-guided theory with hands-on, instructor-led workshops, so you learn by building.

9

Modules, from the fundamentals to production.

25+

Hands-on workshops led by instructors.

100%

Applied learning, with real-world projects and tools.

Blended

Self-guided e-learning plus live workshops.

Competencies

What you will master

Machine Learning

Foundations, regression, decision trees, ensembles, hyperparameter tuning and validation strategies.

Deep Learning

Neural networks with PyTorch, CNNs for computer vision, and RNNs, LSTMs, and GRUs for sequential data.

Generative AI and LLMs

Transformers, prompt engineering, efficient fine-tuning with LoRA, RAG, and application development with Gradio.

MLOps and production

Code packaging, containerisation with Docker, inference contracts, logging and drift monitoring.

Explainability and ethics

XAI with SHAP and Captum, bias mitigation, fairness evaluation and auditable model governance.

Security and optimisation

Threat modelling, defence against prompt injection, performance and cost optimisation, and sustainable engineering.

Syllabus

The syllabus, module by module

Nine modules covering the entire AI lifecycle, from fundamentals to production. Each with its key technologies and technical level. You can choose the modules that are necessary for your team.

1
Fundamentals of Machine Learning

Pandas

Regression

Time series

Code level

2
Practical Machine Learning techniques

Decision trees

Ensembles

Tuning

Code level

3
Product Management for AI

CRISP-ML(Q)

MLflow

Monitoring

Code level

4
Neural networks and Deep Learning

PyTorch

CNNs

RNNs and LSTMs

Code level

5
Explainability and interpretability

SHAP

Captum

Equity

Code level

6
AI ethics and governance

AI Fairness 360

Model cards

Governance

Code level

7
Generative AI and LLMs

Transformers

LoRA

RAG

Code level

8
Machine Learning in production

Docker

Inference contracts

Monitoring

Code level

9
Security and optimisation of ML systems

Threat modelling

Prompt injection

Optimisation

Code level

Do you want to train your team as AI Engineers?

We adapt the itinerary to your organisation's starting level and objectives. Tell us about your context and we will prepare a proposal with the modules, format and schedule that best suit your team.

Request information

Request information