Hands-on AI, ML and Databricks training for engineering teams.

Engineers set up Claude Code, the command-line coding agent, against real codebases and use it across the development lifecycle. The day covers multi-file refactoring, test generation and debugging loops, steering the agent with CLAUDE.md files and precise instructions, and keeping it inside safe permission and sandbox boundaries.

A hands-on introduction to Generative AI for engineering teams, focused on implementation patterns, technical constraints and realistic system behaviour. Participants build two small GenAI projects and look at common failure modes, evaluation basics and cost. It works well as a pilot before larger architectural commitments.

Developers move from basic scripting to maintainable, testable AI-enabled Python code. The programme covers engineering patterns and modular package design, then LLM integration with the OpenAI and Anthropic APIs, agent development with LangChain or Agent Bricks, and the basics of evaluation, monitoring and cost control.

How GenAI prototypes become maintainable, accountable systems. The focus is architectural discipline and operational responsibility rather than tooling trends: system design, pipelines and observability, cost and model selection, and testing. This is the core programme for organisations moving from pilots to production.

An intensive programme covering the full RAG lifecycle from ingestion to generation. Participants implement and compare the main variants, including sparse, dense, hybrid, reranked, query-rewritten and graph-based retrieval, and evaluate them on realistic use cases.

For teams building complex, data-intensive systems. The programme looks at where classical machine learning and Generative AI complement each other and where clear architectural trade-offs are needed, covering LLM system design, retrieval and vector workflows, orchestration versus fine-tuning, and scalability.

A hands-on programme that takes practitioners from single-prompt LLM use to multi-step, tool-using and multi-agent systems. It covers reliable prompting, RAG and reasoning patterns, orchestration with tools and memory, multi-agent coordination and evaluation, worked through realistic business scenarios.

A hands-on introduction to building and evaluating predictive models that bridges software engineering and data science. Participants follow the machine learning lifecycle from preprocessing and feature selection to training and deployment, working in Jupyter or Databricks notebooks.

Moves teams from basic predictive modelling to production machine learning systems. The course covers ensemble methods and neural network foundations, then the operational side: experiment tracking and model versioning with MLflow, performance tuning, and serving and monitoring for batch and real-time inference.

A strategic overview of Generative AI and Machine Learning for executives and senior leaders: what these technologies can and cannot realistically deliver in an enterprise. The aim is decision clarity rather than technical depth, supported by demonstrations of industry-specific use cases.

A compact programme for executives and department heads who need to build a data-driven culture and handle AI governance and regulation. It combines non-technical AI concepts with work on the participants' own challenges, ending in a prioritised use-case roadmap and an initial Proof-of-Value analysis.

A case-based programme for senior managers and executives leading AI-driven change, with a clear, non-hyped view of what current AI can and cannot do. Participants map AI opportunities to their value chain, frame and prioritise initiatives, and develop governance for foundation models and agentic systems.

A single end-to-end walk through the Databricks Data Intelligence Platform, from the workspace to a first look at AI and machine learning. Participants find and govern data in Unity Catalog, write SQL and notebook code with AI assistance, build a dashboard and explore data in natural language with Genie.

A hands-on introduction to governance on Databricks with Unity Catalog. Participants work through the permission model, catalog and schema setup, discovery and lineage, and fine-grained controls such as row-level security, column masking and attribute-based access, finishing with a governance lab.

A combined introduction for teams that do both data engineering and data science on Databricks. The first part builds a Medallion pipeline from ingestion to a scheduled job; the second covers exploratory analysis, feature engineering, MLflow tracking, AutoML and model serving.

Two modules for analysts working on Databricks. The first covers data discovery in Unity Catalog, ingestion, Databricks SQL and query analysis; the second builds, publishes and operates a multi-page AI/BI dashboard. The course covers the topics of the Databricks Certified Data Analyst Associate exam.

An introduction to Databricks Genie One for business users. Participants learn what Genie, Genie One and Genie Agents are, ask plain-language questions about their organisation's data, schedule reports, and see how answers stay governed and secure.

Production ETL pipelines written in pure SQL. Participants use streaming tables with Auto Loader for incremental ingestion, materialized views for Silver-to-Gold transformations, AUTO CDC for slowly changing dimensions and Lakeflow Jobs for orchestration.

The entry point to production data engineering on Databricks, built from four half-day modules. Participants ingest batch, incremental and streaming data, orchestrate workloads with Lakeflow Jobs, build declarative pipelines with data quality checks and CDC, and apply testing, Git and deployment practices.

Two half-day modules for experienced data engineers. The first covers production streaming patterns in Spark Declarative Pipelines: multi-flow ingestion, Multiplex streaming, SCD Type 2 and quarantine pipelines. The second covers diagnosing and fixing performance problems with the Spark UI, data layout and cluster choice.

A four-module course in Apache Spark programming with Python. Participants learn Spark's distributed architecture and the DataFrame API, build ETL and analytics logic, write fault-tolerant streaming applications with Structured Streaming, and monitor and optimise workloads with Delta Lake and the Spark UI.

Covers what Generative AI can do, where it delivers business value and how to implement it securely on Databricks, then gives a practical introduction to the platform's GenAI tools. Participants build and evaluate a retrieval pipeline and see how agents are deployed.

Covers the full machine learning lifecycle on Databricks in four modules. Participants prepare data and features, develop and tune models with MLflow tracking, deploy them in batch, pipeline and real-time modes, and design and monitor MLOps solutions.

Two modules for experienced practitioners. The first covers Spark's architecture for ML, Spark ML and distributed tuning with Optuna; the second covers advanced MLOps: CI/CD, testing and rollout strategies, data profiling and multi-environment deployments with bundles.

Four modules on building generative AI applications on Databricks. Participants build retrieval agents with Agent Bricks and vector search, create governed tools and multi-agent systems, evaluate agents with MLflow judges and human feedback, and deploy and monitor agents in production.

How to design, build and maintain a Genie Agent that answers business questions over governed data with SQL-backed answers. Participants define benchmarks, curate the Knowledge Store, share the agent through Genie One and use monitoring and feedback to improve accuracy over time.