Courses
29 courses, from your first steps in Python to AI systems in production. Every course names what you need before you start, so you can begin at the right place.
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Career
AI Career Foundations: Roles, Skills and Roadmaps
See what people in AI really do, find your starting point, including the experience you already have, and make a realistic plan to get there.
Foundations
Python for Practical AI Engineering
Write, run and debug small Python programs, and organise them as a project that someone else can set up and use.
Foundations
Essential Mathematics and Statistics for AI
The mathematics and statistics behind AI, shown with real examples and charts you can change. You learn what the numbers mean and how to use them in decisions, not how to calculate by hand.
Data and machine learning
SQL and Data Preparation for AI
Get the data for an AI project out of a database, clean it, and prepare it for a model without leaking the answer. You write real SQL in the browser from the first lesson.
Data and machine learning
Machine Learning: From Problem to Reliable Model
Train a model that you can trust, from the business question to a handoff that another person can run. You build a ticket escalation classifier step by step, and test it honestly at every step.
Data and machine learning
Deep Learning and Transformers Explained
See how a neural network learns, from one neuron to a small transformer. You train a network that routes Larkfield's support tickets, find out why its training goes wrong and fix it, and then open a real pretrained language model to see what is inside.
LLM applications
LLMs: How They Work and How to Choose One
Learn what a large language model really does, from tokens to post-training, what it can and cannot do, and how to choose one for your own task with evidence instead of a leaderboard.
LLM applications
Building Reliable Applications with LLM APIs
Build an application that uses a language model and stays reliable when the model is wrong, slow or unavailable. You build Larkfield's ticket assistant step by step, from one request to validated answers, safe tools and tested failure paths.
LLM applications
RAG: Building AI That Uses Your Data
Build an assistant that answers from your own documents and shows where every answer comes from. You turn Larkfield's policy documents into a searchable knowledge base, measure what retrieval finds and misses, and make the assistant say "the documents do not say" when they do not.
LLM applications
AI Agents and Workflow Orchestration
Build a system that resolves support tickets step by step, and stays safe while it does. You turn Larkfield's help desk work into a workflow that looks up evidence, proposes an action, waits for a person's approval before any change, and records what happened.
LLM applications
Evaluating and Testing AI Systems
Find out whether a new version of an AI system is really better, and whether it broke anything that matters. You build an evaluation harness for Larkfield's support assistant, with a versioned set of tickets, scores you can trace, human and model judges you have checked, and a release decision you can defend.
Data and machine learning
Fine-Tuning and Adapting AI Models
Decide with evidence whether to fine-tune a model, and if you do, do it so that you can trust and maintain the result. You prepare clean training data, train a small LoRA adapter on an ordinary computer, compare it with the prompt-only system on the same held-out tickets, and decide whether it ships.
LLM applications
Document and Multimodal AI
Read receipts, scans, photos, tables and policy documents with code and AI, and show exactly where every value came from. You build Larkfield's document pipeline, compare the PDF text layer, OCR and a vision model on the same pages, and send only the uncertain values to a person.
Engineering and deployment
Git and Team Development Essentials
Track your work with Git, work with a team through branches and pull requests, and recover from common mistakes.
Engineering and deployment
AI-Assisted Software Development
Use a coding assistant without losing control of your code. You give it a bounded task with clear evidence of done, read every line it changes, check the packages it suggests, and debug with tests instead of blind rewrites, on your own ticket-cleaner from the Python course.
Engineering and deployment
Build and Deploy Your First API
Build a small web API with FastAPI, document it with OpenAPI and Swagger UI, test it with Postman and pytest, and deploy it.
Engineering and deployment
Deploying AI Models for Real Users
Serve the model that you trained in Machine Learning to real users. Package it with its contract, choose batch, online or asynchronous serving, build and measure the prediction service, and release a new version with a tested way back.
Engineering and deployment
Docker and Cloud Essentials for AI Developers
Package the ticket API in a container image, run it with a database on your computer, and deploy it to Azure Container Apps with its settings outside the image, a cost check and a full clean-up.
Engineering and deployment
CI/CD, Testing and Safe Releases
Build a pipeline that checks every change, builds one image, and promotes it to staging and production with an approval. Then release an AI model change safely, migrate a database without downtime, roll back and ship a hotfix.
Engineering and deployment
Databases and Storage for AI Applications
Give an AI application a real storage design. You turn Larkfield's ticket API into a service that owns its data in PostgreSQL, keeps files in object storage and vectors next to the records, stays fast on large data, and can be restored after a mistake.
Engineering and deployment
Scaling APIs and AI Workloads
Get an AI service ready for ten times the usual traffic, with proof. You measure Larkfield's ticket API first, then add concurrency, a safe cache, a job queue with workers, and limits that keep overload under control, and you show every change with a load test.
Engineering and deployment
Frontend Architecture and Performance for AI Apps
Build a fast, clear and safe screen for AI work. You turn Larkfield's ticket API into a real help-desk interface with React and TypeScript, show AI drafts as they are written, track slow AI jobs, and prove every speed-up with a measurement in the browser.
Engineering and deployment
Authentication, Permissions and Multi-Tenant Applications
Make one API safe for two organizations. You add sign-in, roles and organization boundaries to Larkfield's ticket API, so that every request answers three questions on the server — who is calling, what may they do, and in which organization — and you prove it with tests that try to break it.
Engineering and deployment
AI Security, Privacy and Responsible Design
Find out how an AI assistant can be tricked, and make sure a tricked assistant still cannot do harm. You list the risks of Larkfield's support assistant, attack it with harmless injections and fix the consequences in code. Then you map its personal data, design human review, and finish with a security test set and an incident plan.
Engineering and deployment
MLOps and LLMOps: Operating AI in Production
Keep an AI service working for its users after it goes live. You make Larkfield's ticket service operable one module at a time, with owners, objectives, versions, telemetry, monitoring, safe releases and runbooks, and you catch a bad release before customers tell you.
Engineering and deployment
AI System Design and Cost Engineering
Design an AI product before you build it, and know what it will cost. You write Larkfield's design and cost proposal for a document assistant that several shops share, one module at a time. Every number in it is measured, a published price or a stated assumption.
Career
Portfolio Capstone: Build, Evaluate and Ship an AI Product
Build, evaluate and ship your own AI product, with the evidence to show it works. You watch a complete document assistant for two shops being built step by step, and you build your own beside it, one lesson at a time.
LLM applications
MCP: Connect AI Applications to Tools and Data
Connect an AI application to tools and data once, with a standard protocol, and keep control of what it may do. You build one MCP server for Larkfield's support knowledge, inspect it, connect an AI application to it, and protect it with sign-in, permissions and a person's approval.
Career
Leading AI Products and Teams
Lead AI work that you do not build yourself. You make the decisions that Larkfield's leaders really faced, from evidence that the technical courses recorded, and you finish with an AI product and team plan for your own field.
Coming later
We publish each course when beginners have tested it. Hours are estimates and include practice.