How to Break into MLOps

Why MLOps?

MLOps bridges the gap between data science experimentation and production ML systems. As companies deploy more models, the demand for engineers who can operationalize ML has skyrocketed.

Core Skills to Develop

1. Model Serving & Deployment

Learn to deploy models using TensorFlow Serving, TorchServe, or Triton Inference Server. Understand REST vs gRPC endpoints, batching strategies, and A/B testing for models.

2. Feature Stores

Master tools like Feast or Tecton. Understand online vs offline feature stores, feature freshness, and how to prevent training-serving skew.

3. Pipeline Orchestration

Get comfortable with Airflow, Prefect, or Kubeflow Pipelines. Build DAGs that handle data ingestion, preprocessing, training, evaluation, and deployment.

4. Monitoring & Observability

Implement data drift detection, model performance monitoring, and alerting. Tools like Evidently AI, Whylabs, or custom Prometheus metrics are essential.

Recommended Learning Path

1. Month 1-2: Docker, Kubernetes basics, CI/CD fundamentals
2. Month 3-4: ML pipeline tools (MLflow, DVC, Weights & Biases)
3. Month 5-6: Infrastructure as Code (Terraform), cloud ML services
4. Month 7+: Build an end-to-end project and contribute to open source

Interview Tips

Expect system design questions like "Design a real-time recommendation pipeline" and debugging scenarios around model degradation in production.