MLOps Engineer

MLOps Engineers run the production lifecycle of machine learning models. They build the platforms that take models from a notebook to a reliable production system. They own experiment tracking, model registries, feature stores, training infrastructure, model serving, drift detection, and automatic retraining. The bridge between data science and production engineering.

Before you start

• One cloud provider to a working level (AWS, Azure, or GCP)
• Docker and Kubernetes fundamentals
• Terraform for infrastructure as code
• CI/CD pipelines (GitHub Actions or GitLab CI)
• Python to a confident, practical level
• Basic observability (Prometheus, Grafana)

The roadmap

1. ML Fundamentals

   Supervised, unsupervised, and reinforcement learning, train/validation/test splits, evaluation metrics, overfitting, and the bias-variance tradeoff. You do not need to be a researcher but you need fluency.

2. Experiment Tracking (MLflow)

   Log every training run with parameters, metrics, artifacts, and the data version it used. The foundation of reproducibility in ML.

3. Model Registry and Versioning

   The source of truth for what is deployed. Versions, aliases or stages, promotion, rollback, and lineage back to training runs.

4. Feature Stores (Feast)

   One definition for features across training and inference. Online layer for low-latency reads, offline layer for batch training reads. Prevents train and serve skew.

5. Training Infrastructure (SageMaker, Vertex AI, Databricks)

   Where training jobs actually run. Pick one cloud's managed training platform and learn it deeply. The patterns transfer to the others.

6. Distributed Training (Ray, DeepSpeed)

   When one GPU is not enough. Data parallelism, model parallelism, pipeline parallelism, DDP, and ZeRO.

7. Inference Serving (BentoML, KServe, Triton)

   Where models actually serve traffic. BentoML for Pythonic serving, KServe for Kubernetes-native, Triton for high-throughput GPU serving.

8. Inference Optimisation

   Making models faster and cheaper. Quantisation (FP16, INT8, INT4), ONNX for portability, TensorRT for NVIDIA GPUs, batching, and caching.

9. Deployment Patterns

   How models roll out safely. Canary for gradual rollout, blue-green for fast cutover, shadow for validation, A/B testing for business impact.

10. CI/CD for ML

    Pipelines for data validation, model training, and deployment. The three pipelines of a mature ML platform. GitHub Actions plus your orchestrator.

11. Model Monitoring and Drift Detection (Evidently)

    Watch the live model. Performance metrics, data drift, prediction drift, concept drift, and alerting on degradation.

12. Retraining Pipelines and Continuous Training

    Closing the loop. Drift detected, retraining triggered, new model registered, deployed. The self-healing ML system.

This is the free version. The full one, with what good looks like for every skill and a production capstone project to prove it, is what I teach inside my community. Send me a DM if you want to know more.