Roadmap

MLOps Roadmap — 2025

Phase 1: Core Foundations

Goal

Understand the principles, lifecycle, and motivations behind MLOps.

Key Topics

  • What MLOps is and why it is needed
  • Differences between MLOps and DevOps
  • End-to-end ML lifecycle: data collection → preprocessing → training → evaluation → deployment → monitoring
  • Core principles: reproducibility, scalability, automation, versioning, ML-focused CI/CD

Tools to Learn

  • Python, Git, GitHub
  • Jupyter Notebook or VS Code
  • Docker basics
  • Command line and Linux fundamentals

Practice Ideas

  • Build a small ML project (e.g., Iris classifier)
  • Version control code with Git
  • Containerize the project with Docker

Phase 2: Data Management & Versioning

Goal

Ensure datasets remain consistent, versioned, and reproducible.

Key Topics

  • Data pipeline fundamentals
  • Data and metadata versioning
  • Metadata and lineage management
  • Feature stores as reusable data assets
  • Data validation workflows

Recommended Tools

  • DVC (Data Version Control)
  • Git + DVC for dataset and model tracking
  • Great Expectations or Evidently AI for data validation
  • Feast as a feature store

Practice Ideas

  • Create a DVC repository to version datasets and models
  • Add validation steps before training
  • Prototype a lightweight feature store with Feast

Phase 3: Experiment Tracking & Model Versioning

Goal

Record experiments, hyperparameters, and model versions to ensure reproducibility.

Key Topics

  • Experiment management and traceability
  • Hyperparameter tuning best practices
  • Model versioning and artifact management

Recommended Tools

  • MLflow for tracking and model registry
  • Weights & Biases (W&B) or Neptune.ai for experiment logging
  • Optuna or Ray Tune for hyperparameter search

Practice Ideas

  • Integrate MLflow into training scripts
  • Log metrics, parameters, and artifacts for every run
  • Register finalized models in MLflow

Phase 4: CI/CD for ML Pipelines

Goal

Automate validation, training, testing, and deployment workflows.

Key Topics

  • CI/CD fundamentals for ML
  • Differences between DevOps CI/CD and MLOps CI/CD
  • Automating data validation, training, testing, and deployment stages

Recommended Tools

  • GitHub Actions, GitLab CI, or Jenkins for automation
  • Airflow or Prefect for orchestration
  • Docker Compose for local integration testing

Practice Ideas

  • Build a CI pipeline triggered by data changes
  • Add automated model testing and deployment steps

Phase 5: Workflow Orchestration

Goal

Design scalable, maintainable ML pipelines.

Key Topics

  • Directed acyclic graphs (DAGs) for pipeline design
  • Scheduling, retries, and dependency management
  • Coordinating training and deployment jobs

Recommended Tools

  • Apache Airflow
  • Kubeflow Pipelines
  • Prefect or Dagster

Practice Ideas

  • Implement an Airflow DAG for data preprocessing, model training, evaluation, and deployment triggers

Phase 6: Model Deployment & Serving

Goal

Learn production-ready model deployment and serving strategies.

Key Topics

  • Batch, real-time, and streaming inference patterns
  • Packaging models with Docker and APIs
  • REST API design for inference
  • A/B testing, canary deployments, and rollback strategies

Recommended Tools

  • FastAPI or Flask for serving APIs
  • TensorFlow Serving or TorchServe
  • ONNX for cross-framework deployment
  • Kubernetes (K8s) for scalable serving

Practice Ideas

  • Deploy a model via FastAPI and Docker
  • Add inference logging
  • Experiment with local Kubernetes (e.g., Minikube)

Phase 7: Monitoring & Observability

Goal

Keep deployed models healthy, observable, and reliable.

Key Topics

  • Model performance monitoring
  • Data drift and concept drift detection
  • Real-time dashboards and alerting
  • Logging, metrics, and tracing

Recommended Tools

  • Evidently AI for model monitoring
  • Prometheus +Grafana for metrics and visualization
  • ELK Stack (Elasticsearch, Logstash, Kibana) for log analytics

Practice Ideas

  • Monitor drift between training and production data
  • Configure Prometheus alerts for accuracy drops or latency spikes

Phase 8: Cloud & Infrastructure

Goal

Deploy and manage ML systems on cloud infrastructure.

Key Topics

  • Cloud fundamentals across AWS, GCP, and Azure
  • Container orchestration strategies
  • Infrastructure as Code (IaC)
  • Security, IAM, and cost management

Recommended Tools

  • AWS SageMaker, GCP Vertex AI, or Azure ML
  • Terraform or AWS CloudFormation
  • Kubernetes + Helm for scalable ML infrastructure

Practice Ideas

  • Deploy a containerized model to AWS ECS or GCP Vertex AI
  • Automate infrastructure provisioning with Terraform

Phase 9: Advanced MLOps (Scaling & Retraining)

Goal

Build automated retraining and scaling workflows.

Key Topics

  • Automated and triggered retraining pipelines
  • Model drift detection and retraining policies
  • Online learning and continual training
  • Distributed training patterns
  • Advanced feature store usage

Recommended Tools

  • Airflow + MLflow + Docker + FastAPI stacks
  • Ray or Horovod for distributed training
  • Feast or Tecton for advanced feature management

Practice Ideas

  • Automate retraining when new data arrives
  • Schedule recurring retraining and deployment pipelines

Phase 10: End-to-End Capstone Project

Goal

Integrate all MLOps concepts into a production-ready ML system.

Project Ideas

  • Customer churn prediction: data → model → CI/CD → deployment → monitoring
  • Fraud detection pipeline: batch + streaming inference, drift monitoring
  • NLP sentiment analysis service: API-based serving with versioned data pipelines
  • Image classification on GCP: GPU training, Vertex AI deployment, real-time monitoring

Expected Deliverables

  • End-to-end pipeline (data → model → deployment → monitoring)
  • Public GitHub repository with MLflow tracking and CI/CD automation
  • Cloud deployment with metrics dashboards

Bonus: Interview & Practical Readiness

Conceptual Questions to Master

  • Explain each component of an MLOps pipeline
  • Distinguish between data drift and concept drift
  • Describe the role of CI/CD in ML
  • Outline strategies for model reproducibility
  • Compare MLflow, DVC, and Weights & Biases

Hands-On Skills to Demonstrate

  • Dockerize and containerize ML workloads
  • Track experiments with MLflow
  • Automate pipelines with Airflow (or equivalent)
  • Deploy on Kubernetes or major cloud providers
  • Monitor models with Evidently AI or similar tools

Portfolio Checklist

  • Public GitHub repository with at least one end-to-end project
  • CI/CD pipeline configuration
  • Demonstrated usage of MLflow or DVC
  • FastAPI (or similar) deployment example
  • Cloud deployment demo on AWS, GCP, or Azure

Suggested 8-Week Timeline

Week Focus Outcome
1 Foundations & lifecycle Mini ML project
2 Data versioning & validation DVC + validation tooling
3 Experiment tracking MLflow integration
4 CI/CD fundamentals Automated training pipeline
5 Deployment (FastAPI + Docker) Model served as REST API
6 Orchestration & monitoring Airflow + Evidently setup
7 Cloud deployment & scaling Model deployed on AWS/GCP
8 Capstone & interview prep Production-ready pipeline + prep

Key Learning Path Summary

  • Learn DevOps basics: Docker, CI/CD, GitHub Actions
  • Master the ML lifecycle: data, modeling, evaluation
  • Version everything: datasets, models, code
  • Automate pipelines and retraining routines
  • Deploy effectively: APIs, Kubernetes, cloud services
  • Monitor continuously: drift detection, logging, metrics
  • Build a portfolio: showcase 1–2 automated, production-ready projects