Practical ML Ops for Data Scientists
Take your ML models from notebook to production — CI/CD, model registries, monitoring, and MLOps practices.
Taught by Aki Wijesundara, PhD
Co-founder · COO · Lead Instructor · San Francisco / London
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Aki Wijesundara, PhD
Lead instructor
7 hours
Built to fit around work
4 modules
15 lessons
3 applied labs
Evidence, not just watching
Interactive course
Online
Built for real work
Leave with work you can show.
Every part of the course moves towards an applied result. You will practise the judgement behind the tools, build with them and finish with evidence of what you can do.
Automate a retraining and release path
Monitor drift and cost in production
Who it is for
Right level. Clear expectations.
This is a intermediate course for people who want to use the material, not collect another set of notes.
Data scientist
Use the exercises and final project in the context of your own role.
ML engineer
Use the exercises and final project in the context of your own role.
Course syllabus
A complete path, not a content dump.
4 modules · 15 lessons · 3 applied labs · 7 hours.
- The map: how Practical ML Ops for Data Scientists fits your workBriefing · 16m
- Interactive walkthrough: one worked example, start to finishInteractive · 22m
- Knowledge check: spot the difference between good and plausibleKnowledge check · 12m
Your instructor
Aki Wijesundara, PhD
Co-founder · COO · Lead Instructor · San Francisco / London
AI/ML lead with a PhD in Machine Learning and former Head of AI at Mainstreet Partners. Stanford-backed founder, Ex-Google AI Accelerator mentor; leads enterprise deliveries for engineering teams.
What learners say
Practical enough to use the next day.
Real, attributed feedback from people learning with TAI Labs.
“In just two live sessions I walked away with a reusable GTM playbook, launch templates, and the AI prompts to run every future launch faster.”
Irena Palamani Xhurxhi, PhD
Director of People Science · Walmart
“The asynchronous format made it easy to catch up and focus on the parts most relevant to my job — practical, and immediately useful.”
Alexandros Fragkos
Marketing Analytics Manager · Google
“In only four weeks we went from LLM basics to building our own production-grade app — multi-agent workflows, RAG with vector search, and real evaluation.”
Anette
Product Manager · Apple
Start with the course. Keep the bigger path visible.
Take this course on its own, use it inside the Career Accelerator, or bring a private version to your team. The right route depends on how much support and applied experience you need.