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
Wrapping a model in a reliable API
Wrapping a model in a reliable API
Turn a trained model into a small, well behaved web service that fails clearly and safelyMalik Brennan$5Medical imaging AI: how it is built and checkedMedical imaging AI: how it is built and checked
Understand how imaging models are trained, validated and overseen, for education onlyMalik Brennan$8CI and testing for ML projectsCI and testing for ML projects
Add fast automated checks that catch broken data, code and models before they reach usersMalik Brennan$8AI visual inspection for factories, explainedAI visual inspection for factories, explained
Learn how camera based defect detection works on a production line, and what makes it succeedMagnus Eriksen$5On device and edge modelsOn device and edge models
Decide when a model should run on the phone or device itself, and make it fit and run wellMagnus Eriksen$6From Software Engineer to ML EngineerFrom Software Engineer to ML Engineer
Use your engineering strengths to move into ML engineering with a focused plan for maths and MLYohannes Tesfaye$11Teachers 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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