Rahul Sengupta
Program Director — Data
Former Head of Data at a logistics platform; now shapes HIGAET's data engineering track.
Data Science · Career Track
Data Engineering & MLOps at Scale
The data-to-model pipeline that actually survives reorgs and audits.
Duration
9 months
Format
Online
Fee
₹2,55,000
Next start
Next cohort: 16 November 2026
Curriculum
A 9 months arc, term by term.
- Term 1 · Foundations (10 weeks)
Data platforms
- Pipeline architecture (batch + streaming)
- Lakes, warehouses, lakehouses
- Data quality & observability
- Term 2 · Core (10 weeks)
Features & pipelines
- Feature stores and serving
- Governance, lineage, and cost
- Performance and scale
- Term 3 · MLOps (10 weeks)
MLOps at scale
- Experiment tracking & registries
- Training and serving pipelines
- Drift, skew, and rollback
- Term 4 · Capstone (8 weeks)
Platform capstone
- Live brief from a data partner
- Architecture review
- Production deployment
Eligibility
Who this program is for
- Bachelor's degree (any quantitative or technical discipline preferred)
- Comfortable with SQL and Python for data work
- Familiarity with cloud concepts (compute, storage, networking)
Fees & financing
Transparent pricing
- Total fee
- ₹2,55,000
- EMI from
- ₹7,700/mo
- Scholarships
- Up to 60% via HAT
Outcomes
What you'll be able to do.
- Design batch and streaming pipelines with contracts, lineage, and quality gates
- Build feature stores that serve both retrieval and training reliably
- Run MLOps pipelines from experiment → registry → deployment → drift response
- Own data platform operability: cost, governance, and audit readiness
Faculty
Practitioners, not lecturers.
Pooja Nair
Lead Faculty — MLOps
Operates ML platforms serving 50M+ predictions per day.
Apply
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
FAQ
Common questions
Ready to join Data Engineering & MLOps at Scale?
Next cohort: 16 November 2026