Best of MLOps — March 2026

1. 1
   Article

   Towards Data Science·10w

   Machine Learning at Scale: Managing More Than One Model in Production

   Managing a portfolio of ML models in production requires a fundamentally different mindset than single-model deployments. Key challenges include prioritizing availability over perfection (using safe fallbacks when models fail), the limitations of traditional accuracy metrics at scale, infrastructure decisions around cloud vs. device and tiered GPU/CPU strategies, and the near-invisible risk of label leakage across complex data pipelines. Practical safeguards include feature latency monitoring, shadow deployments, and human-in-the-loop auditing for high-stakes models.

   19
2. 2
   Article

   freeCodeCamp·9w

   How to Build an End-to-End ML Platform Locally: From Experiment Tracking to CI/CD

   A comprehensive hands-on guide to building a local end-to-end ML platform for fraud detection. Starts by exposing the pitfalls of a naive ML approach (no experiment tracking, no model versioning, no data validation, no monitoring, no CI/CD), then incrementally adds MLflow for experiment tracking and model registry, Feast as a feature store, FastAPI for model serving, Great Expectations for data validation, Evidently for drift monitoring, Docker for containerization, and GitHub Actions for CI/CD. All code is copy-paste runnable and targets local execution without cloud or Kubernetes.

   13
3. 3
   Article

   Kubeflow·8w

   Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience

   Kubeflow SDK v0.4.0 introduces a ModelRegistryClient for managing model artifacts and versions via a Pythonic API, a SparkClient with SparkConnect support for interactive distributed data processing on Kubernetes without YAML, namespaced TrainingRuntimes for better multi-tenant isolation, and Dataset/Model Initializers to improve parity between local and remote execution. The release also raises the minimum Python version to 3.10 and launches a dedicated documentation website. Future roadmap items include MCP server integration, MLflow support, Kubeflow Pipelines unification, LLM training, and multi-cluster job submission.

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