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Foundations 89

All Foundations89 AI basics25 How language models work23 AI safety and ethics49

Everyday AI 91

All Everyday AI91 Prompting20 ChatGPT and Claude at work25 AI for writing29 AI for research and study33

Building with AI 162

All Building with AI162 Python for AI21 Building with LLM APIs30 RAG and search21 Evaluation and testing21 AI agents31 AI automation25 AI coding assistants30

Machine learning 149

All Machine learning149 Machine learning35 Data science and statistics30 Math for AI23 Deep learning28 NLP14 Computer vision14 Fine tuning and training12 MLOps and deployment18

Creative AI 77

All Creative AI77 Image and video AI58 Audio and voice AI12 AI for design33

AI at work 145

All AI at work145 AI for business34 AI for marketing and sales23 AI for product and operations28 AI for education28 AI for professionals29 AI careers18

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MLOps and deployment Clear all

Inference optimisation for serving at scale

Inference optimisation for serving at scale

Serve language models faster and cheaper by understanding prefill, decode, batching and cachingAdvancedMLOps and deployment4.7(3)83 lessonsSampleMagnus Eriksen$15 Planning the cost of a training run

Planning the cost of a training run

Estimate compute, time, memory and budget for a training or fine tuning run before you spendAll levelsFine tuning and training4.3(3)65 lessonsSampleMagnus Eriksen$7 MLOps for a team of one

MLOps for a team of one

Put a model into use responsibly with the few practices that matter when you work aloneBeginnerMLOps and deployment4.7(3)62 lessonsSampleMalik BrennanFree Monitoring models and catching drift

Monitoring models and catching drift

Notice when a live model starts getting worse, even before the true answers arriveIntermediateMLOps and deployment4.7(3)58 lessonsSampleMalik Brennan$9 Experiment tracking you will actually use

Experiment tracking you will actually use

Log runs so you can compare, reproduce and explain results months later, with any toolBeginnerMLOps and deployment4.3(3)58 lessonsSampleMalik Brennan$4 Batching and caching for model inference

Batching and caching for model inference

Serve more requests on the same hardware by batching smartly and caching what can be reusedIntermediateMLOps and deployment4.7(3)57 lessonsSampleMagnus Eriksen$9 GPUs and AI hardware for beginners

GPUs and AI hardware for beginners

Understand what GPUs do for AI and estimate whether a model will fit on your hardwareBeginnerMLOps and deployment4.7(3)55 lessonsSampleMagnus EriksenFree Incident response for ML systems

Incident response for ML systems

Detect, contain and learn from ML failures, from silent quality drops to harmful outputsAll levelsMLOps and deployment4.7(3)51 lessonsSampleMalik Brennan$7 Quantisation: smaller, faster models

Quantisation: smaller, faster models

Shrink models with lower precision numbers and measure exactly what quality you trade awayIntermediateFine tuning and training4.3(3)49 lessonsSampleMagnus Eriksen$9 Versioning models and data

Versioning models and data

Know exactly which data and code produced every model, and recover or delete them when neededBeginnerMLOps and deployment4.7(3)48 lessonsSampleMalik Brennan$5 Deploying an open weights model

Deploying an open weights model

Choose, size, secure and run an open weights model in production, and compare its real costIntermediateMLOps and deployment4.7(3)46 lessonsSampleMagnus Eriksen$9 Model cards and ML governance

Model cards and ML governance

Document models honestly and set up light, real governance that helps people make good decisionsBeginnerAI safety and ethics4.0(3)45 lessonsSampleMalik Brennan$5