MLOps Roadmap 2025

Your complete 30-day journey to master Machine Learning Operations

Follow this structured learning path day by day to build production-ready ML systems

Day Area / Tool Description Purpose Resources
Day 1 Intro to MLOps icon 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
Day 2 Tools landscape icon MLOps Tools Landscape Survey of tools across data, training, tracking, serving, and monitoring. Choose the right stack for your team and constraints. Landscape
Day 3 DVC icon Data Versioning with DVC Version datasets and models alongside code with remote storage backends. Reproduce experiments and collaborate on data changes safely. DVC
Day 4 Docker icon 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
Day 5 Feature store icon Feature Engineering & Feature Stores Design features, prevent training/serving skew, and manage feature reuse. Standardize features for online/offline access with governance. Features
Day 6 Scikit-learn icon 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
Day 7 MLflow icon Experiment Tracking with MLflow Log params, metrics, artifacts; compare runs; record lineage. Make results auditable and improve iteration speed. MLflow
Day 8 Metrics icon Model Evaluation & Metrics Select task-appropriate metrics and build robust validation strategies. Ensure models generalize and meet business/ethical targets. Metrics
Day 9 Kubeflow icon ML Pipelines with Kubeflow Pipelines Compose training workflows as versioned, parameterized components. Automate and scale pipelines with reproducibility. KFP
Day 10 FastAPI icon Serving ML Models with FastAPI & Flask Expose inference via HTTP with input validation and health checks. Deliver low-latency, reliable predictions to clients. APIs
Day 11 Docker icon Packaging Models with Docker Bundle code, model, and system deps into immutable images. Enable portable deployments across environments. Docker Image
Day 12 GitHub Actions icon 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
Day 13 Deployment rocket icon ML Model Deployment Strategies: blue/green, canary, A/B; infra as code for rollouts. Release models safely with rollback and monitoring hooks. Deploy
Day 14 Drift detection icon Data Drift & ML Model Drift Detection Detect data distribution and performance shifts post-deployment. Alert, investigate, and trigger retraining when quality drops. Drift
Day 15 Automated retraining icon Automated Retraining ML Pipelines Schedule retraining jobs based on drift or calendar windows. Keep models fresh and aligned to changing data. Retrain
Day 16 Security icon Security in MLOps – Protecting ML Systems at Every Layer Safeguard ML systems from threats like data poisoning, model theft, and adversarial attacks. Secure every layer—from data pipelines to model deployment—for reliability, compliance, and trust. Implement security best practices across the ML lifecycle: data, models, pipelines, endpoints, and governance. Security
Day 17 XAI icon Explainable AI (XAI) in Production – SHAP, LIME, and Interpretability Techniques Ensure transparency, trust, and accountability in model predictions using SHAP, LIME, and interpretability techniques for compliance, debugging, and trust-building. Integrate XAI into production APIs, dashboards, and monitoring flows to support regulated and high-stakes ML deployments. XAI
Day 18 Governance icon ML Model Governance & Compliance – Auditing, Explainability & Fairness in ML Ensure accountability, traceability, and fairness in machine learning systems using auditing, explainability tools, and bias detection for compliant AI solutions. Build trustworthy and compliant ML systems that align with legal and ethical standards (GDPR, HIPAA, SOC2). Govern
Day 19 Prometheus icon Monitoring ML Systems in Production – Metrics, Logging, Alerting Master monitoring to ensure ML models in production are healthy, reliable, and performant using Prometheus, Grafana, and custom logging solutions. Track key metrics, analyze logs, and set up alerts to proactively detect model drift, data quality issues, and service failures. Monitor
Day 20 Model registry icon Model Registry – Managing and Versioning ML Models Systematically manage, version, and track ML models across their lifecycle, ensuring reproducibility and smooth transitions between development, staging, and production. Enable better collaboration, auditability, and automated model promotion in real-world ML workflows using MLflow, SageMaker, and DVC. Registry
Day 21 Kubernetes icon Scaling ML Model Inference with Kubernetes Learn scaling model inference with Kubernetes to ensure high availability and low-latency predictions in production ML systems. Master dynamic autoscaling, cost optimization, and seamless deployment of AI workloads at scale using HPA, LoadBalancers, and Ingress. K8s
Day 22 Cloud platforms icon MLOps with ML Platforms (SageMaker & Vertex AI) Learn platforms like SageMaker and Vertex AI that offer end-to-end managed services for model training, deployment, and monitoring. Master these platforms to enable faster experimentation, scalable automation, and production-grade ML workflows with built-in security and governance. Platforms
Day 23 OpenAI icon Managing LLMs in Production Prompt/version management, safety filters, cost and latency controls. Operate LLM apps reliably with observability and guardrails. LLMs
Day 24 Agentic & RAG icon Agentic AI & RAG Retrieve-augmented generation and tool-using agents for production apps. Improve accuracy and autonomy with controlled knowledge access. RAG
Day 25 MCP icon MCP Explained for MLOps Engineers Use Model Context Protocol to integrate tools and orchestrate workflows. Standardize interfaces between AI systems and platform tools. MCP
Day 26 Project icon Project: End-to-End MLOps Pipeline Hands-on build: data → training → registry → deploy → monitor → retrain. Apply all concepts in a realistic, reproducible project. Project
Day 27 Serverless icon 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
Day 28 Cost & performance icon Cost & Performance Tuning in MLOps Profiling, quantization, batching, and right-sizing infrastructure. Optimize ROI while meeting SLAs. Optimize
Day 29 Disaster recovery icon Disaster Recovery & HA for ML Systems Backups, multi-region, chaos testing, and failover strategies. Design resilient ML services for business continuity. Resilience
Day 30 Interview Q&A icon 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