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MLOps Roadmap
What is MLOps & Why It Matters
DevOps vs MLOps
Version Control with Git
Foundations
ML Lifecycle (Data → Training → Deployment → Monitoring)
Containers & Virtual Environments (Docker, venv, Conda)
Data Collection & Labeling
Feature Engineering & Feature Stores
ETL/ELT Pipelines for ML
Data Engineering for MLOps
Data Validation & Quality Checks
Data Versioning (DVC, LakeFS)
Experiment Tracking (MLflow, Weights & Biases, Neptune)
Model Versioning
Experimentation & Model Development
Hyperparameter Tuning
Reproducibility & Deterministic Training
Continuous Integration Basics
Pipeline Automation (Jenkins, GitHub Actions, GitLab CI)
Continuous Training (CT)
CI/CD for ML
Unit Testing for ML Code
Model Testing & Validation
Continuous Deployment (CD)
Batch Inference
Model Serving (TensorFlow Serving, TorchServe, KFServing)
A/B Testing & Canary Releases
Deployment Strategies
Real-time Inference APIs
Serverless Deployments (AWS Lambda, GCP Cloud Functions)
Monitoring System Metrics (Latency, Throughput, Resource Usage)
Logging & Tracing
Tools: Prometheus, Grafana, EvidentlyAI
Monitoring & Observability
Model Monitoring (Accuracy, Drift, Fairness)
Alerting & Incident Response
Kubernetes for ML
Airflow for Orchestration
Distributed Training (Horovod, Ray)
Scaling & Infrastructure
Kubeflow & ML Pipelines
Cloud Platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
Cost Optimization
Feature Store Architectures
Bias & Fairness in Production ML
Federated Learning Pipelines
Advanced Topics
Model Governance & Compliance
Model Explainability (SHAP, LIME, Captum)
LLMOps (MLOps for Generative AI & LLMs)
Build a CI/CD Pipeline for ML Model
Real-time Prediction Service with Kubernetes
End-to-End MLOps Project with Kubeflow
Projects
Deploy a Model with Docker & FastAPI
Model Monitoring Dashboard with Prometheus & Grafana
MLOps Core Concepts Q&A
Deployment Strategy Scenarios
Case Studies (Netflix, Uber, Google MLOps)
Interview Preparation
CI/CD for ML Questions
Monitoring & Drift Handling Questions
