MLOps Roadmap 2025

A curated path to master MLOps with the same theme and style.

Note: We have 15 days of topics available now — the rest will be updated soon.

Area / Tool	Description	Purpose	Resources
Intro to MLOps: ML Meets DevOps	Principles that combine ML workflows with DevOps culture and automation.	Understand goals, lifecycle stages, roles, and where MLOps fits.	Overview
MLOps Tools Landscape	Survey of tools across data, training, tracking, serving, and monitoring.	Choose the right stack for your team and constraints.	Landscape
Data Versioning with DVC	Version datasets and models alongside code with remote storage backends.	Reproduce experiments and collaborate on data changes safely.	DVC
Reproducible ML Environments (Conda & Docker)	Lock dependencies for training and inference across machines and CI.	Eliminate "works on my machine" and ensure portable builds.	Envs
Feature Engineering & Feature Stores	Design features, prevent training/serving skew, and manage feature reuse.	Standardize features for online/offline access with governance.	Features
Training with Scikit-learn & TensorFlow	Author, train, and serialize models using common ML/DL frameworks.	Build baseline to advanced models ready for evaluation and packaging.	Training
Experiment Tracking with MLflow	Log params, metrics, artifacts; compare runs; record lineage.	Make results auditable and improve iteration speed.	MLflow
Model Evaluation & Metrics	Select task-appropriate metrics and build robust validation strategies.	Ensure models generalize and meet business/ethical targets.	Metrics
ML Pipelines with Kubeflow Pipelines	Compose training workflows as versioned, parameterized components.	Automate and scale pipelines with reproducibility.	KFP
Serving ML Models with FastAPI & Flask	Expose inference via HTTP with input validation and health checks.	Deliver low-latency, reliable predictions to clients.	APIs
Packaging Models with Docker	Bundle code, model, and system deps into immutable images.	Enable portable deployments across environments.	Docker Image
CI/CD for ML with GitHub Actions	Automate tests, linting, builds, and model checks on every change.	Ship reliable ML with gated, reproducible pipelines.	CI/CD
ML Model Deployment	Strategies: blue/green, canary, A/B; infra as code for rollouts.	Release models safely with rollback and monitoring hooks.	Deploy
Data Drift & ML Model Drift Detection	Detect data distribution and performance shifts post-deployment.	Alert, investigate, and trigger retraining when quality drops.	Drift
Automated Retraining ML Pipelines	Schedule retraining jobs based on drift or calendar windows.	Keep models fresh and aligned to changing data.	Retrain
Security in MLOps	Secrets, supply chain, image scanning, PII handling, policy enforcement.	Protect data, models, and pipelines from threats.	Security
Explainable AI (XAI) in Production	Use SHAP/LIME and model-specific methods for transparent predictions.	Build trust, debug models, and meet regulatory needs.	XAI
ML Model Governance & Compliance	Policies, approvals, audit trails, and risk management for ML.	Operate responsibly under legal and ethical frameworks.	Govern
Monitoring ML Systems in Production	Collect infra, app, and ML-specific telemetry; set SLOs and alerts.	Maintain reliability and catch regressions fast.	Monitor
Model Registry	Manage model versions, stages (staging/prod), and approvals.	Standardize promotion workflows and traceability.	Registry
Scaling Inference with Kubernetes	Autoscaling, node/pod tuning, GPUs, and scheduling for inference.	Handle traffic spikes and latency budgets efficiently.	K8s
MLOps with ML Platforms	Leverage managed platforms (SageMaker, Vertex, Azure ML) end-to-end.	Accelerate delivery with built-in integrations and SLAs.	Platforms
Managing LLMs in Production	Prompt/version management, safety filters, cost and latency controls.	Operate LLM apps reliably with observability and guardrails.	LLMs
Agentic AI & RAG	Retrieve-augmented generation and tool-using agents for production apps.	Improve accuracy and autonomy with controlled knowledge access.	RAG
MCP Explained for MLOps Engineers	Use Model Context Protocol to integrate tools and orchestrate workflows.	Standardize interfaces between AI systems and platform tools.	MCP
Project: End-to-End MLOps Pipeline	Hands-on build: data → training → registry → deploy → monitor → retrain.	Apply all concepts in a realistic, reproducible project.	Project
Model Deployment with Serverless Architectures	Use Functions-as-a-Service and managed APIs for bursty inference.	Achieve low ops overhead and pay-per-use efficiency.	Serverless
Cost & Performance Tuning in MLOps	Profiling, quantization, batching, and right-sizing infrastructure.	Optimize ROI while meeting SLAs.	Optimize
Disaster Recovery & HA for ML Systems	Backups, multi-region, chaos testing, and failover strategies.	Design resilient ML services for business continuity.	Resilience
MLOps Interview Questions & Answers	Role-focused Q&A covering pipelines, infra, monitoring, and LLM ops.	Prepare for interviews with practical, scenario-based prompts.	Q&A