MLOps
MLOps is an intermediate, 8-week course for learners who already have a trained model, Python skills and basic deployment concepts. It teaches packaging and operating a predictive model with reproducible training pipelines, feature stores, drift monitoring, and automated retraining with rollback, culminating in a versioned prediction service project with data contracts and a retraining review workflow.
Package and operate a predictive model with reproducible artifacts, feature contracts, monitoring and rollback.
What you'll learn
Course details
Duration
8 weeks
Best for
Learners who already have: A trained model, Python and basic deployment concepts.
Enrollment isn't open for this course yet — check back soon or contact us.
Course Curriculum
A module-by-module breakdown of what you'll cover in MLOps.
01Basic
6 topicsWeeks 1–2
- What is MLOps, and why do ML models need special operational practices?
- Model versioning and experiment tracking
- Reproducible training pipelines
- Data validation and schema testing before training
- Model registries and the model promotion lifecycle
- Project: Build a reproducible, tracked training pipeline
02Intermediate
6 topicsWeek 3
- Feature stores and feature contracts
- Model deployment patterns: batch, real-time, and shadow deployment
- Model monitoring: performance metrics and data drift
- Containerizing and serving models with Docker and Kubernetes
- Canary releases and A/B testing for model rollouts
- Project: Build a model deployment with drift detection
03Advanced
4 topicsWeeks 4–5
- Keep training and inference transformations consistent
- Evaluate a prediction system against an honest baseline
- Package the model with its input contract and provenance
- Serve predictions through a validated operational boundary
04Pro
6 topicsWeek 6
- Automated retraining pipelines
- Rollback strategies and model governance
- CI/CD for machine learning pipelines
- Compute cost management for training and inference at scale
- Multi-model serving and champion/challenger testing
- Project: Build an automated retraining pipeline with rollback
05AI Pro
3 topicsWeeks 7–8
- Using Claude, ChatGPT, and Perplexity for MLOps pipeline design and debugging
- Verifying AI-suggested MLOps automation before trusting it with real models
- Project: Use AI to build promotion logic, then verify both outcomes explicitly
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
Real-Time, Industry-Based Projects
This is project-based learning modeled on real industry scenarios — you'll build portfolio-ready work as you go, not just watch lectures.
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Practical AI Skills
Hands-on training in using AI tools well — prompting, everyday workflows, and where AI actually helps versus where it doesn't.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Career & Placement Support
100% of students enrolled in MLOps get full access to placement support — real tools and guidance, not just a promise. This is support and access, not a guaranteed job outcome.
Resume Builder
A resume auto-built from your real completed projects and certificates, ready to export.
Shareable Portfolio
A public portfolio page showcasing your approved projects, with a link you can share with employers.
Interview Preparation
Guidance on presenting your projects and experience clearly in real interviews.
Peer & Mentor Community
Ongoing access to our learner community for questions, feedback, and support after you finish.
What Your Certificates Look Like
Real, verifiable certificates with your name, MLOps, and a QR code anyone can scan to confirm they're genuine — one for completing the course, and a separate one if you take the internship track. Earning a certificate is free. To view, download and share it, there's a one-time ₹100 release fee per certificate — the same for free-course, paid-course and internship certificates.
Course Completion Certificate
Data24ZoneCertificate of Completion
Illustrative recipient
Your Name Here
Example certificate for completion of
MLOps
- Certificate No.
- D24-SAMPLE
- Mode
- Self-Paced
- Duration
- 8 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Internship Completion Certificate
Data24ZoneCertificate of Internship
Illustrative recipient
Your Name Here
Example certificate for completion of
MLOps
- Certificate No.
- D24-SAMPLE
- Mode
- Internship
- Duration
- 8 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Frequently Asked Questions
What will I learn in the MLOps course?
You'll learn: Keep training and inference transformations consistent; Evaluate a prediction system against an honest baseline; Package the model with its input contract and provenance; Serve predictions through a validated operational boundary.
What tools and technologies does the MLOps course cover?
This course covers: MLflow, Python, ChatGPT, Claude, Perplexity.
Is the MLOps course beginner-friendly?
This is a intermediate-level course, so it assumes some prior familiarity with the subject.
How long does the MLOps course take to complete?
The course is designed to be completed in approximately 8 weeks, depending on your pace.
What enrollment options are available for MLOps?
Project Training Track (₹1,499) — One defined learning project and 2 review sessions; no employment or stipend included. Self-Paced (₹1,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for MLOps?
Yes — the Internship track means working through real projects with our team and earning a separate Internship Certificate.
Will I get a certificate after completing MLOps?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once your internship projects are approved. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile. Earning it is free; releasing it to view, download and share is a one-time ₹100 fee per certificate.
What kind of projects will I build in MLOps?
You'll work on real projects such as: Versioned prediction service with data contracts and a retraining review workflow.
Is placement support included with MLOps?
Yes — every enrolled student gets full access to our placement support: resume building, a shareable project portfolio, mock interview preparation, and guidance applying what you've built to real job applications. This is support and access, not a guaranteed job outcome — how quickly it leads to an offer depends on the market and your own effort.
