MLOps & Machine Learning Platform Engineering
MLOps bridges data science models and production software engineering. Master automated ML training pipelines (Kubeflow, Vertex AI), model registry and versioning (MLflow), feature stores (Feast), low-latency GPU serving (Triton Inference Server, vLLM), data/concept drift monitoring (Evidently AI), and zero-downtime canary model deployments.
🇮🇳 Indian Market Benchmark
Core Track Highlights
End-to-End MLOps Pipeline & Continuous Training (CT) Loop
Feature store, experiment tracking, automated model training DAG, Triton serving, and drift detection.
Feature Store (Feast)
Unified point-in-time correct features for offline training and low-latency online inference.
Experiment Tracking (MLflow)
Tracking hyperparameters, metrics, artifacts, and registering candidate models.
Containerized Serving (Triton)
Concurrent model execution, dynamic batching, and TensorRT optimization.
Drift & Retraining Triggers
Monitoring Population Stability Index (PSI) and triggering automated retraining.
Structured Phase-by-Phase Syllabus
Focus on build-by-doing milestones rather than passive video consumption.
Phase 1: Experiment Tracking, Packaging & Feature Stores
- MLflow tracking: Logging metrics, parameters, code hashes, and model artifacts
- Building online/offline Feature Stores with Feast to prevent training-serving skew
- Data versioning with DVC (Data Version Control) linked to cloud object storage (S3/GCS)
Phase 2: Automated Pipelines on Kubernetes & Model Serving
- Containerizing ML pipelines with Docker and orchestrating training DAGs on Kubeflow / Airflow
- High-performance model serving with Triton Inference Server and FastAPI (Dynamic batching, TensorRT)
- Deploying shadow deployments, A/B model splits, and canary rollouts using Istio on Kubernetes
Phase 3: Production Monitoring, Drift & Continuous Training (CT)
- Data drift vs Concept drift: Calculating Wasserstein distance and Population Stability Index (PSI)
- Real-time model monitoring using Evidently AI and Prometheus/Grafana alerts
- Building Continuous Training (CT) pipelines triggering automated retraining on drift detection
Technical Interview Questions & Answers
Q1: What is Training-Serving Skew and how do you prevent it in an MLOps architecture?
Training-Serving Skew occurs when the feature values or data transformations used during model training differ from the values computed during real-time production inference. It is prevented by: (1) Using a centralized Feature Store (like Feast) that guarantees identical point-in-time feature transformation logic for both batch training and online lookup, (2) Packaging data preprocessing pipelines inside the serialized model artifact itself (e.g. Scikit-learn Pipeline or ONNX graph), and (3) Continuous data drift monitoring.
Frequently Asked Questions
What is the difference between Data Science and MLOps?
Data Scientists focus on exploratory data analysis, feature engineering, and model accuracy metrics; MLOps engineers focus on infrastructure, automated CI/CD pipelines, containerization, low-latency deployment, model drift monitoring, and 99.99% system availability.
Target Job Roles
MLOps Engineer / ML Platform Engineer
Demand: Very HighStaff MLOps Architect / AI Infrastructure Lead
Demand: HighRelated Career Tracks
Need a Personalized Career Plan?
Take our 20+ Signal Career Compass to assess aptitude and discover suitable roadmaps.
Start Career Compass