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Browse instructor-led programmes in Generative AI, Agentic AI and NVIDIA GPU AI, plus data engineering, cloud, analytics and DevOps. Every course can be customised for your team.
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Generative AI for Data Teams
LLM fundamentals, prompt design, retrieval-augmented generation and responsible use of GenAI on enterprise data.
AI Assistants at Work
Practical, safe use of LLM-based AI assistants for drafting, summarising, analysis and everyday productivity.
AI Assistants for Power Users (Advanced)
Go beyond basics: structured prompting, document workflows, custom instructions, tool integrations and team guidelines.
Generative AI Fundamentals
How large language models work, what they can and cannot do, and how to apply them responsibly in business.
LLMOps: Deploying LLM Applications
Take LLM applications to production: serving, evaluation, observability, cost control and security.
RAG Engineering: Retrieval-Augmented Generation in Production
Design, build and evaluate RAG systems that answer reliably from your own documents, with citations and access control.
Generative AILLM Fine-Tuning: LoRA, QLoRA & Evaluation
Adapt open-weight language models to your tasks with supervised fine-tuning and parameter-efficient methods, then prove the improvement.
Prompt Engineering for Teams
Practical prompt design, structured outputs and verification habits for professionals and developers using LLMs daily.
Generative & Agentic AI for Leaders
A non-technical programme for executives and managers: what GenAI and agents can do, how to pick use cases, and how to govern risk.
Agentic AIAgentic AI with LangChain & LangGraph
Build tool-using agents and stateful workflows with LangChain and LangGraph, with evaluation and guardrails.
Agentic AIBuilding AI Agents with LangGraph & CrewAI
Build tool-using agents and agent teams with two popular open-source frameworks, and learn when to use each.
Multi-Agent Systems: Design & Orchestration
Design systems where several specialised agents collaborate: orchestration patterns, communication, evaluation and governance.
Agentic AI for Business Process Automation
Identify, design and pilot agent-driven automations for operations, support and back-office processes, safely.
GPU Programming with CUDA
Learn to write and optimise GPU code with CUDA, NVIDIA's parallel computing platform, in C++ and Python.
Accelerated Data Science with NVIDIA RAPIDS
Speed up pandas, scikit-learn-style ML and Spark workloads on GPUs with the open-source RAPIDS libraries.
Building and Serving LLM Apps with NVIDIA NIM & NeMo
Deploy models as NVIDIA NIM inference microservices and customise, evaluate and guard them with NeMo tools.
Agentic AI on the NVIDIA Stack
Build and profile AI agents using NVIDIA NIM models and the open-source NeMo Agent toolkit alongside frameworks such as LangGraph.
Inference Optimisation with TensorRT & TensorRT-LLM
Make models faster and cheaper to serve: TensorRT for deep learning inference and TensorRT-LLM for large language models.
Model Serving with NVIDIA Triton Inference Server
Serve models from multiple frameworks at scale with Triton: model repositories, dynamic batching, ensembles and monitoring.
GPU Infrastructure & DGX Fundamentals
Understand and operate AI infrastructure: GPUs, DGX systems, networking, scheduling and monitoring for AI workloads.
Data Engineering Foundations
Pipelines, warehouses, lakes and the modern data stack explained hands-on, from first principles to a working end-to-end pipeline.
Data EngineeringApache Spark & PySpark for Data Engineers
Distributed processing with DataFrames, Spark SQL, partitioning, joins and performance tuning on realistic datasets.
Databricks & Lakehouse Engineering
Delta Lake, medallion architecture, workflows, Unity Catalog concepts and production-grade lakehouse patterns.
Data EngineeringWorkflow Orchestration with Apache Airflow
DAG design, scheduling, retries, sensors, testing and running reliable pipelines in production.
Data EngineeringReal-Time Data with Kafka & Streaming
Event streaming fundamentals, producers/consumers, topics, schema handling and stream processing patterns.
Azure Data Engineering
Build ingestion and transformation on Azure using Data Factory, ADLS, Synapse-style analytics and Databricks.
AWS Data Engineering
S3-based lakes, Glue, Athena, Redshift and orchestration patterns for analytics workloads on AWS.
Google Cloud & BigQuery for Data Teams
BigQuery modelling, Dataflow concepts, cost-aware querying and data pipelines on Google Cloud.
AWS Solutions Architecture
Design resilient, secure, cost-aware architectures on AWS, aligned with the Well-Architected Framework.
Google Cloud Fundamentals
Core Google Cloud services for compute, storage, networking, IAM and data.
Azure Fundamentals
Cloud concepts and core Azure services, governance and pricing basics.
Azure Administration
Manage Azure identities, compute, storage, networking and monitoring.
Cloud Computing Fundamentals
Vendor-neutral introduction to cloud models, services, security and cost.
AWS Cloud Foundations
Core AWS concepts, services, security and billing, a strong base for cloud roles.
Azure Solutions Architecture
Design identity, governance, data, continuity and infrastructure solutions on Azure.
Advanced SQL for Data Professionals
Window functions, CTEs, query plans, modelling for analytics and writing SQL that scales.
Power BI for Business Analytics
Data modelling, DAX, Power Query and dashboard design for decision-makers.
Analytics & BIPython for Data Engineering & Analysis
Python, pandas and automation for cleaning, transforming and moving data reliably.
Analytics & BIData Science for Professionals
Statistics, experimentation and predictive modelling applied to real business questions.
Data Analyst Programme
An end-to-end analyst track: Excel, SQL, a BI tool and storytelling with data.
Tableau for Visual Analytics
Build interactive dashboards with calculated fields, LOD expressions and sound visual design.
Advanced Excel for Analytics
Power Query, PivotTables, the Data Model and modern Excel functions for faster analysis.
Machine Learning & MLOpsMLOps Foundations
Taking models from notebook to production: versioning, pipelines, deployment and monitoring.
Machine Learning & MLOpsMachine Learning Fundamentals
Core machine learning concepts, algorithms and evaluation, explained with practical examples.
Machine Learning & MLOpsMachine Learning with Python
Hands-on ML with pandas and scikit-learn, from data preparation to tuned, validated models.
DevOps for DataDevOps, Docker & CI/CD for Data Platforms
Containers, Git workflows, CI/CD and infrastructure-as-code applied to data platforms.
DevOps for DataDocker & Kubernetes
Containerise applications and run them on Kubernetes with deployments, services and configuration.
DevOps for DataSite Reliability Engineering for Data Platforms
Apply SLOs, error budgets, monitoring and incident management to data platforms and pipelines.
DevOps for DataCI/CD with Jenkins & GitLab
Design build, test and deployment pipelines with Jenkins and GitLab CI/CD.
Terraform & Infrastructure as Code
Provision cloud infrastructure with Terraform: modules, state, workspaces and CI integration.
Ansible Automation
Automate configuration and deployments with playbooks, roles and inventories.
DevOps on AWS
CI/CD, infrastructure as code, containers and observability using AWS services.
Cybersecurity Fundamentals
Threats, identity, network and cloud security basics, and secure habits for technical teams.
Software DevelopmentReact for Front-End Engineers
Build modern user interfaces with React components, hooks, routing and testing.
Software DevelopmentFront-End Development Foundations
HTML, CSS, JavaScript and accessibility fundamentals for building responsive web interfaces.
Pick a role. See the path.
GenAI Engineer
Prompt engineering → RAG → Fine-tuning → LLMOps
Agentic AI Developer
GenAI fundamentals → LangGraph & CrewAI agents → Multi-agent systems
GPU / Inference Engineer
CUDA → TensorRT-LLM → Triton → NIM & NeMo
Data Engineer
Python & SQL → Spark → Airflow → Cloud data engineering → Capstone
Analytics Engineer
Advanced SQL → Data modelling → Power BI → Orchestration
ML / AI Engineer
Python for data → MLOps foundations → GenAI for data teams
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