MLOps Certification Course with Placement Support
Build & Deploy Production ML Pipelines • Instructor-Led • Live Projects
Program fee
₹60,000
One-time payment
MLOps course fees are ₹60,000 for live instructor-led training with capstone and certificate.
Payment options
- One-time payment for 5-month live instructor-led program.
Prerequisites (kept practical)
Comfort with Python, Git, and basic ML concepts (train/validate). Docker/Kubernetes basics help, but we cover essentials before deeper orchestration.
Data Quality & Validation
Stop bad data before it breaks training and production
Orchestration & Pipelines
Repeatable workflows with retries, scheduling, and observability
Streaming & Event Ingestion
Real-time ingestion patterns for modern ML systems
Versioning & Reproducibility
Track data + models like software; reproduce runs anytime
Experiment Tracking & Registry
Track metrics/artifacts and manage model promotion
Containerization & Kubernetes
Package once, deploy anywhere, scale reliably
Model Serving & Scaling
Latency, throughput, rollout control, and multi-model serving
Monitoring, Drift & Reliability
Know when things break — and why
Cloud Track (Optional)
Same MLOps patterns mapped to managed cloud services
The Complete MLOps Lifecycle
Learn the production ML workflow used by engineering teams to ship reliable, monitored models at scale.
Data Management
Versioning & Quality
Version datasets, validate schemas, track lineage, and monitor data quality end-to-end.
1
Model Development
Experiment Tracking
Track experiments, params, metrics and artifacts with MLflow / W&B for reproducible iteration.
2
CI/CD Pipeline
Automated Testing
Automate training, tests, validation gates and releases with disciplined ML delivery workflows.
3
Model Registry
Version & Promote
Promote models across stages with approvals, metadata and rollback-ready version control.
4
Production Serving
Deploy & Scale
Deploy containers, scale services, run canaries, and ship safe rollouts with load balancing.
5
Monitoring & Ops
Drift & Reliability
Monitor latency, errors, model metrics, drift, and trigger retraining with incident response habits.
6
Data Management
Versioning & Quality
Version datasets, validate schemas, track lineage, and monitor data quality end-to-end.
Model Development
Experiment Tracking
Track experiments, params, metrics and artifacts with MLflow / W&B for reproducible iteration.
CI/CD Pipeline
Automated Testing
Automate training, tests, validation gates and releases with disciplined ML delivery workflows.
Course Curriculum
Packaging ML
Module 1: Containers
- Docker and Containerization
- Reproducible images
- Local-to-cloud parity
Module 2: Versioning
- Data and Model Versioning
- DVC
- MLflow registry
Release discipline
Module 3: CI/CD for ML
- Training pipelines
- Eval gates
- Rollback patterns
Module 4: Kubernetes for ML services
- Serving
- Autoscaling
- Environments
Operate
Module 5: Monitoring
- Model Monitoring
- Drift detection
- Evaluation Pipelines
Frequently asked questions
Why Now
Companies productize AI → need reliable pipelines Compliance & cost control push for strong Ops Upskilling wave among engineers in India
Ready to start?
Talk to an advisor about this program — 15 minutes, no sales pitch.