School of Scientific Computing & Informatics · Data science & AI
MLOps & AI Engineering for Life Sciences
Most machine-learning models in biology never leave a notebook. This course teaches the engineering that turns a promising model into a reliable, monitored service that scientists can depend on.
Course overview
MLOps & AI Engineering for Life Sciences covers the lifecycle of a model: tracked experiments, versioned data and code, reproducible training pipelines, packaging, serving and monitoring for drift once the model meets new data.
The course pays particular attention to what life-science settings demand: provenance, validation documentation, applicability domains and the expectations of quality and regulatory teams.
Learning outcomes
By the end of the course you will be able to:
- Track experiments and version data, code and models
- Build reproducible training pipelines
- Package and serve models behind an API
- Monitor predictions, detect drift and trigger retraining
- Document models for validation and audit
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates in any science, engineering, pharmacy, medicine, agriculture, geography or computing programme (B.Sc., B.Tech., B.E., BCA, M.Sc., M.Tech., MCA, PhD)
- Researchers, faculty and working professionals in research, healthcare, biotech, pharma or IT who want to apply computing to science
- Final-year students may apply before their results are declared
Prerequisites
- Machine Learning for Biology or equivalent
- Docker basics
Best suited to
- Data scientists and ML engineers in biotech and pharma
- Computational biologists deploying models
- Students targeting AI-engineering roles in life sciences
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Why models fail in production
- Project structure
- Configuration management
Topics
- Experiment tracking with MLflow
- Data versioning with DVC
- Model registries
Topics
- Packaging models
- REST APIs with FastAPI
- Batch versus online inference
Topics
- Data and concept drift
- Applicability-domain checks at inference
- Alerting and retraining
Topics
- Model cards and validation reports
- Audit trails
- Expectations in regulated settings
Topics
- Pipeline
- Service
- Monitoring
Practical sessions and projects
Capstone project
Put a model into production
Deploy a biological model as a tracked, monitored and documented service and show how it behaves when the data drift.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- From notebook to pipeline
Refactor a notebook model into a configurable training pipeline.
- Tracking and versioning
Track experiments and register the best model with its data version.
- Serving models
Serve a QSAR model behind an API in a container.
- Monitoring
Detect drift when the model receives compounds outside its training space.
- Validation and governance
Write a validation report and model card for your deployed model.
- Project: a production model
Deploy a biological ML model with tracking, serving, monitoring and documentation.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- ChEMBL-derived QSAR sets: Model to deploy.ChEMBL
- Public expression classifiers: Drift scenarios.GEO
Core reading
- Sculley D et al. (2015). Hidden technical debt in machine learning systems. NeurIPS 28.
- Mitchell M et al. (2019). Model cards for model reporting. FAT* ’19.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for MLOps & AI Engineering for Life Sciences, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for MLOps & AI Engineering for Life Sciences
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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