Testing
Deploying a new ML model version is riskier than deploying a new API version. A bad API deployment returns errors — observable, alertable, rollback-triggering. A bad model deployment returns predictions that look valid but are wrong. Accuracy can degrade 20% before a business metric moves enough to alert. By then
MLOps
Weights & Biases (W&B) tracks ML experiments — but it also provides the infrastructure to assert training quality, detect regressions, and gate deployments on metric thresholds. This post covers how to use W&B as a testing tool, not just a logging tool. W&B as a
MLOps
OpenAI Evals is a framework for evaluating large language model outputs.
MLOps
Evidently AI is an open-source framework for evaluating and monitoring machine learning models.
MLOps
Seldon Core and KServe deploy machine learning models as Kubernetes-native inference services.
MLOps
BentoML packages machine learning models as production-ready API services.
Testing
Great Expectations (GX) is the de facto standard for data quality validation in ML pipelines. It lets you define data expectations as code, validate datasets before training, and generate visual data quality reports. This guide covers setup, writing expectations, checkpoints, and pipeline integration. Why Data Validation Matters for ML The
Testing
ML models degrade silently as the world changes. Data drift tests verify that incoming features match training distribution. Concept drift tests detect when the relationship between features and labels has changed. This guide covers both with Evidently, Alibi Detect, and CI integration. The Drift Problem You deploy a credit risk
Testing
MLflow provides the infrastructure for systematic ML model testing: experiment tracking for comparison, model validation for quality gates, and a model registry for staging promotion. This guide covers all three with practical test patterns for CI/ML pipelines. Why ML Models Need Tests Software tests assert that code does what
Machine Learning
Feature stores solve the problem of feature reuse and training/serving consistency in ML systems.
Machine Learning
A model you deploy today will degrade over time. The world changes — user behavior shifts, economic conditions fluctuate, upstream data systems evolve.
Machine Learning
Offline evaluation tells you a model performs better on held-out data.