Machine Learning Engineer
Train, evaluate and serve models that hold up in production.
- Data preparation
- Model training
- Evaluation
- Experiment tracking
Make model training and deployment reproducible and observable.
Live, interactive classes with time for every learner's questions and code reviews.
Request this batch A 5–10 learners batch starts once enough learners ask. You confirm and pay only when dates are set.Python, Git and basic container knowledge.
Start at the level that matches your experience. Each module links to its course in the catalogue.
Programming basics, tools and guided exercises.
End-to-end implementation, testing and deployment.
Architecture, reliability, security, optimisation and technical decisions.
A short skills check places you at Foundation, Practitioner or Advanced and, for experienced learners, identifies the gaps worth closing.
Each module ends with a practical task reviewed against clear criteria before you move on.
Your capstone — a reproducible training-to-deployment pipeline with a model registry and drift checks — is reviewed against a role-specific rubric.
Optional 1:1 sessions with Ajay for project reviews, career guidance and unblocking — arranged separately.
Explore mentorshipFinishing the MLOps Engineer path is planned to include the support below. It opens with the first batch; enrolment pages confirm what is included before you pay.
Issued with a unique ID once your final project passes review. It records what you built, not just attendance.
Certification policyPortfolio and GitHub review, resume feedback, a mock interview and referrals when suitable openings come up. No placement guarantee.
Career mentorshipGuidance on pricing, proposals and your first client project, plus a review of your freelance profile and portfolio.
Build client-style projectsAn invite to the Innovate with Ajay community to meet builders, find co-founders, join hackathons and get feedback on product ideas.
Explore the communityShare your interest. We’ll confirm availability, batch dates and the fee separately, so you can decide when the details are ready.
Train, evaluate and serve models that hold up in production.
Turn data into evidence, models and clear decisions.