Teacher since May 2026
Malik Brennan
MLOps without the ceremony: tracking, versioning, monitoring and responsible deployment
9
tutors built
4.5Sample
average from 18 reviews
322Sample
lessons taught by their tutors
About Malik
I teach the habits that keep machine learning systems trustworthy after the notebook: tracking experiments, versioning data and models, testing, monitoring, handling incidents and documenting models honestly. I came to this from software operations, where I learned that most failures are boring and preventable, and then spent years helping small teams put models into production without drowning in tooling. I teach the smallest practice that solves the real problem, explain why it matters with failure stories, and stay neutral about tools. For regulated areas such as healthcare I teach concepts only and always point you to qualified people and local rules.
Knows about
Tutors by Malik
9 tutors
MLOps 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$4Incident response for ML systemsIncident response for ML systems
Detect, contain and learn from ML failures, from silent quality drops to harmful outputs51 lessonsSampleMalik Brennan$7Versioning models and dataVersioning models and data
Know exactly which data and code produced every model, and recover or delete them when needed48 lessonsSampleMalik Brennan$5Model 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$5Wrapping a model in a reliable APIWrapping 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$8Recent reviews
What students said about Malik's tutors.
- Rashid K.Sample
Writing a one sentence hypothesis per run felt silly at first. Two months later it is the most useful column I have.
- Hyun-woo P.Sample
Rewriting our vague out of scope section into concrete uses with reasons changed a product discussion the same week. Very practical.
- Beatriz S.Sample
Good balance between documentation and governance. The tutor was clear it would not give legal advice, which annoyed me briefly but was right.
- Tariq S.Sample
Our postmortems used to end with vague actions. Owners and dates, reviewed later, made a real difference.
- Owen T.Sample
Useful templates and reasoning. For my small team the risk tier lesson felt a bit heavy, though the principles still applied.
- Lucy M.Sample
We cut our alerts from fourteen to four, each with an owner. People actually respond now.