About This Program
A production-focused career program for machine-learning engineers and MLOps practitioners responsible for the complete predictive-ML lifecycle. Learners translate business decisions into measurable ML problems, construct reproducible feature and training pipelines, track experiments, govern model versions, deploy batch and online inference, automate testing and releases, and monitor data and model behavior in production. Reliability, security, fairness, privacy, cost and incident response are integrated into engineering practice rather than added after deployment. Unlike the broad AI Engineer pathway, this specialization concentrates on operating maintainable ML systems at organizational scale. The capstone requires a tested, observable and continuously deliverable ML product with an operational defense.
What You'll Get
20+ Production Machine-Learning Engineering Laboratories
Feature Pipelines, Stores and Point-in-Time Data Design
MLflow Experiment Tracking, Registry and Model Lifecycle
Serving, CI/CD, Monitoring, Security and Reliability
Faculty-Reviewed Production MLOps Capstone
Program Curriculum
1Production Python, SQL & Software Engineering2 Weeks
Key Topics:
Typed Python, Modules, Packaging and Dependency Management
NumPy, Pandas and Memory-Efficient Data Processing
SQL, Data Modeling, Transactions and Analytical Queries
HTTP APIs, FastAPI and Service Integration
Git, Code Review, Unit Testing and Documentation
Linux, Containers, Configuration and Secrets Foundations
2ML Problem Framing, Statistics & Evaluation3 Weeks
Key Topics:
Business Decisions, ML Suitability and Success Criteria
Sampling, Label Design, Leakage and Dataset Splitting
Probability, Statistical Inference and Uncertainty
Metrics, Thresholds, Calibration and Decision Costs
Cross-Validation, Baselines and Model Selection
Offline Evaluation, Online Experiments and Causal Limitations
3Applied Machine Learning & Model Development4 Weeks
Key Topics:
Preprocessing Pipelines and Reproducible Transformations
Linear, Logistic and Regularized Models
Decision Trees, Random Forests and Gradient Boosting
Imbalanced Data, Cost-Sensitive Learning and Ranking
Clustering, Dimensionality Reduction and Anomaly Detection
Hyperparameter Optimization, Diagnostics and Error Analysis
4Feature Engineering & Feature Platforms3 Weeks
Key Topics:
Numerical, Categorical, Temporal and Text Feature Patterns
Event Time, Temporal Joins and Point-in-Time Correctness
Offline and Online Feature Store Architecture
Feature Definitions, Contracts, Ownership and Reuse
Feature Quality, Freshness, Lineage and Backfills
Training-Serving Skew and Feature Retrieval Performance
5Deep Learning for Production Systems3 Weeks
Key Topics:
Tensor Operations, Autograd and Neural-Network Training
Architecture Design, Regularization and Optimization
Embeddings and Applied Vision, Text and Sequence Patterns
GPU Utilization, Mixed Precision and Distributed Training
Checkpointing, Transfer Learning and Reproducible Runs
Model Export, Quantization, Distillation and Inference Optimization
6Data & Training Pipeline Orchestration3 Weeks
Key Topics:
Batch, Streaming and Change-Data-Capture Ingestion
Schema Enforcement, Data Validation and Quality Gates
Reusable Components, DAGs and Pipeline Orchestrators
Dataset, Code, Configuration and Artifact Versioning
Scheduled, Event-Driven and Performance-Triggered Training
Caching, Backfills, Idempotency and Pipeline Recovery
7Experiment Tracking, Registry & Reproducibility3 Weeks
Key Topics:
MLflow Runs, Parameters, Metrics, Artifacts and Lineage
Reproducible Environments, Seeds and Deterministic Limitations
Parallel Experiments, Search Studies and Result Comparison
Model Packaging, Signatures and Dependency Capture
Model Registry, Aliases, Approvals and Environment Promotion
Model Cards, Decision Records and Collaborative Review
8Model Deployment, Serving & Performance4 Weeks
Key Topics:
Batch, Online, Streaming and Edge Inference Patterns
REST and gRPC Model Services with Containerized Runtimes
Preprocessing, Postprocessing and Prediction Contracts
Autoscaling, Queues, Accelerators and Concurrency Control
Latency, Throughput, Availability and Cost Optimization
Shadow, Canary, Blue-Green, A/B and Fallback Deployments
9ML Testing, CI/CD & Infrastructure3 Weeks
Key Topics:
ML Testing Pyramid and Testable System Boundaries
Data, Feature, Model-Behavior and Invariance Tests
Pipeline, API, Contract and End-to-End Testing
CI Builds, Quality Gates, Artifact Scanning and Provenance
Continuous Delivery, Promotion, Rollback and Change Control
Infrastructure as Code, Kubernetes, Cloud IAM and Environments
10ML Observability, Drift & Continuous Improvement3 Weeks
Key Topics:
Service Metrics, Logs, Traces and Prediction Correlation
Data Quality, Schema Change and Distribution Drift
Concept Drift, Delayed Labels and Production Performance
Slice Metrics, Fairness Monitoring and Hidden Regressions
Alert Design, Triage, Incident Response and Root Cause
Retraining Policies, Champion-Challenger and Model Retirement
11Secure, Responsible & Scalable ML Platforms2 Weeks
Key Topics:
ML Threat Modeling, Poisoning, Evasion and Model Extraction
Privacy, Sensitive Data, Access Control and Data Minimization
Fairness, Explainability, Human Oversight and Recourse
Software Supply Chain, Images, Dependencies and Secrets
Governance Evidence, Audit Trails and Segregation of Duties
Platform Architecture, Multi-Tenancy, FinOps and Sustainability
12Production MLOps Capstone & Career Readiness3 Weeks
Key Topics:
ML Product Requirements, Risks and Service Objectives
Data Contracts, Feature Pipeline and Classical Baseline
Training Pipeline, Experiment Tracking and Model Registry
Scalable Serving, Release Strategy and Automated Tests
Monitoring, Drift Response, Security and Incident Runbooks
Faculty Architecture Review, Reliability Exercise and Defense
Engineering Portfolio, ML System Design and Role Interviews
Course Fee
₹1,05,000/ 9 Months
Limited seats available for next batch.