Train-serve pipeline
Automate from commit to a versioned, tested model endpoint.
Specialization Courses/AI & Machine Learning
AI-First Pro specialization
AI-First Pro: MLOps / LLMOps Specialization
Take models out of notebooks and into reliable production.
- Intermediate to advanced
- Instructor-led
- Projects + Experience
- Placement Assurance
Built around practical learning, projects, mentorship, and career readiness.
Real skills. Real solutions. A brighter tomorrow.
What you will learn
Gain in-demand, industry-relevant skill, create meaningful work, and drive smarter decisions with AI as your copilot.
- Design an ML delivery pipeline from training to serving
- Version data, models, and experiments with discipline
- Monitor drift, quality, and cost in production
- Collaborate across data, ML, and platform teams
- Work with Docker, Kubernetes, MLflow
- Prepare toward roles such as MLOps Engineer
Your learning journey
A simple, focused path from learning to real-world impact.
- 1
Learn
Build a strong foundation with live classes, guided content, and hands-on labs.
- 2
Build
Work on real-world projects using industry tools and AI-powered workflows.
- 3
Demonstrate
Create a portfolio, gain practical experience, and get career-ready with mentorship and placement support.
Curriculum at a glance
A structured, hands-on curriculum designed for real-world outcomes.
- APIs
- Packaging
- Environments
- Reproducibility
Projects
Work you can speak about in an interview, not a single weekend demo.
Drift watchdog
Detect silent failure and define a human response path.
Cost-aware serving
Meet latency targets without burning the budget.
Outcomes
What you will be able to do, and the kinds of roles this track prepares you toward.
- Design an ML delivery pipeline from training to serving
- Version data, models, and experiments with discipline
- Monitor drift, quality, and cost in production
- Collaborate across data, ML, and platform teams
MLOps EngineerML Platform EngineerML EngineerAI Infrastructure Associate
FAQs
You need comfort with software engineering basics. The track teaches the ML-specific production layer rather than assuming you already run a platform team.