MLOps and deployment
Ship models to production and keep them healthy: serving, monitoring, updates.
18tutors
3teachers
2free to start
$4 to $15per paid lesson
MLOps and deployment tutors
18 tutors
Inference optimisation for serving at scale
Inference optimisation for serving at scale
Serve language models faster and cheaper by understanding prefill, decode, batching and caching83 lessonsSampleMagnus Eriksen$15Planning the cost of a training runPlanning the cost of a training run
Estimate compute, time, memory and budget for a training or fine tuning run before you spend65 lessonsSampleMagnus Eriksen$7MLOps for a team of oneMLOps for a team of one
Put a model into use responsibly with the few practices that matter when you work alone62 lessonsSampleMalik BrennanFreeMonitoring models and catching driftMonitoring models and catching drift
Notice when a live model starts getting worse, even before the true answers arrive58 lessonsSampleMalik Brennan$9Experiment tracking you will actually useExperiment tracking you will actually use
Log runs so you can compare, reproduce and explain results months later, with any tool58 lessonsSampleMalik Brennan$4Batching and caching for model inferenceBatching and caching for model inference
Serve more requests on the same hardware by batching smartly and caching what can be reused57 lessonsSampleMagnus Eriksen$9GPUs and AI hardware for beginnersGPUs and AI hardware for beginners
Understand what GPUs do for AI and estimate whether a model will fit on your hardware55 lessonsSampleMagnus EriksenFreeIncident response for ML systemsIncident response for ML systems
Detect, contain and learn from ML failures, from silent quality drops to harmful outputs51 lessonsSampleMalik Brennan$7Quantisation: smaller, faster modelsQuantisation: smaller, faster models
Shrink models with lower precision numbers and measure exactly what quality you trade away49 lessonsSampleMagnus Eriksen$9Versioning models and dataVersioning models and data
Know exactly which data and code produced every model, and recover or delete them when needed48 lessonsSampleMalik Brennan$5Deploying an open weights modelDeploying an open weights model
Choose, size, secure and run an open weights model in production, and compare its real cost46 lessonsSampleMagnus Eriksen$9Model cards and ML governanceModel cards and ML governance
Document models honestly and set up light, real governance that helps people make good decisions45 lessonsSampleMalik Brennan$5Teachers who teach MLOps and deployment
They wrote the lesson plans these tutors follow.
Magnus Eriksen
Making models fast, small and affordable: hardware, quantisation, serving and edgeGPUs and accelerators, training cost estimation, quantisation9 tutorsSampleMalik Brennan
MLOps without the ceremony: tracking, versioning, monitoring and responsible deploymentexperiment tracking, model and data versioning, model APIs9 tutorsSampleYohannes Tesfaye
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI rolesML engineering interviews, data science interviews, AI product manager interviews9 tutorsSampleMore in Machine learning
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