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roadmap updated 2026-06-01

MLOps Engineer Roadmap

Productionize machine learning models with robust training pipelines, model registries, feature stores, and deployment patterns. Bridge data science and production engineering for reliable ML systems.

Phase 1 — Beginner

Understand ML development lifecycle, data versioning, and how to package and deploy a model beyond a Jupyter notebook.

MLflowDVCDockerPythonFastAPI

Phase 2 — Intermediate

Build automated training pipelines, model registries, feature stores, and implement model monitoring in production.

KubeflowFeastSeldon CoreBentoMLWeights & Biases

Phase 3 — Advanced

Architect enterprise ML platforms, manage multi-model production fleets, and drive ML governance and responsible AI practices.

Vertex AISageMakerRayKubeflow PipelinesEvidently AI

The path: Beginner → Intermediate → Advanced

Beginner

Focus: Understand ML development lifecycle, data versioning, and how to package and deploy a model beyond a Jupyter notebook.

Skills to build

  • Machine learning fundamentals: training, validation, testing
  • Python for ML: scikit-learn, PyTorch, TensorFlow basics
  • Data versioning with DVC or Delta Lake
  • Experiment tracking with MLflow or Weights & Biases
  • Docker containerization of ML training workloads
  • Git workflows for ML code and configuration
  • Model serialization formats: pickle, ONNX, SavedModel
  • REST API serving with FastAPI for ML models

Tools to learn

  • MLflow
  • DVC
  • Docker
  • Python
  • FastAPI
  • Jupyter

Intermediate

Focus: Build automated training pipelines, model registries, feature stores, and implement model monitoring in production.

Skills to build

  • ML pipeline orchestration with Kubeflow, Airflow, or Prefect
  • Feature store design and real-time vs. batch feature serving
  • Model registry workflows: staging, approval, production promotion
  • A/B testing and shadow deployment for ML models
  • Data drift and model performance drift monitoring
  • Distributed training with Horovod or PyTorch DDP
  • Model serving at scale with Triton Inference Server or TorchServe
  • CI/CD for ML: automated retraining triggers and validation gates

Tools to learn

  • Kubeflow
  • Feast
  • Seldon Core
  • BentoML
  • Weights & Biases
  • Airflow
  • Triton

Advanced

Focus: Architect enterprise ML platforms, manage multi-model production fleets, and drive ML governance and responsible AI practices.

Skills to build

  • ML platform architecture: multi-tenant training and serving infrastructure
  • Continuous training pipelines triggered by data drift signals
  • Model governance: lineage, audit trails, and reproducibility
  • Large-scale model training on GPU clusters with Kubernetes
  • Multi-model serving optimization: batching, quantization, pruning
  • Responsible AI: bias detection, explainability, and fairness metrics
  • ML cost optimization: spot instances, mixed precision, efficient architectures
  • ML platform product management and data scientist enablement

Tools to learn

  • Vertex AI
  • SageMaker
  • Ray
  • Kubeflow Pipelines
  • Evidently AI
  • Tecton
  • Databricks

Labs to practice

Interview questions to prepare

  1. What is the difference between MLOps and DevOps, and what unique challenges does ML introduce?
  2. How do you detect and handle data drift in a production ML model?
  3. Explain the concept of a feature store and why it is important in production ML systems.
  4. How would you design a CI/CD pipeline for an ML model that includes data validation and model evaluation gates?
  5. What is model reproducibility and how do you ensure it across training runs?
  6. How do you decide when to retrain a model versus when to roll back to a previous version?
  7. Explain the trade-offs between online serving and batch inference for ML models.
  8. What observability do you instrument for a deployed ML model in production?

Certification suggestions

  • AWS Certified Machine Learning – Specialty — Amazon Web Services
  • Google Professional Machine Learning Engineer — Google Cloud
  • Databricks Certified Machine Learning Professional — Databricks
  • Deep Learning Specialization Certificate — Coursera/DeepLearning.AI
  • MLOps Specialization Certificate — Coursera/DeepLearning.AI

See exam formats, costs and official links in the certification registry.

Free resources

Portfolio project ideas

  • Build an end-to-end MLOps pipeline using MLflow for experiment tracking, DVC for data versioning, and GitHub Actions for automated retraining on data drift
  • Deploy a scikit-learn model as a REST API with BentoML, containerize it, and set up Evidently AI for data drift and prediction drift monitoring
  • Implement a feature store with Feast backed by Redis for online serving and BigQuery for offline, serving two ML models from the same feature definitions
  • Create a model promotion workflow with staging and production environments, automated evaluation gates, and shadow deployment using Seldon Core

Mistakes to avoid

  • Treating model files like application code in Git — use DVC or an artifact store for large model files
  • Not logging all hyperparameters and metrics — reproducibility requires capturing every variable that affects training outcomes
  • Deploying a model without monitoring — without drift detection and performance tracking, degradation goes unnoticed for weeks
  • Using manual retraining — automating trigger-based retraining prevents models from silently degrading in production
  • Skipping data validation in the pipeline — garbage-in-garbage-out applies doubly in ML where silent data issues compound over time

Keep going