https://www.pistack.xyz/tags/mlops/Recent content in Mlops on Pi StackHugoen-usSun, 19 Apr 2026 00:00:00 +0000https://www.pistack.xyz/posts/feast-vs-featureform-vs-hopsworks-self-hosted-ml-feature-store-2026/Sun, 19 Apr 2026 00:00:00 +0000https://www.pistack.xyz/posts/feast-vs-featureform-vs-hopsworks-self-hosted-ml-feature-store-2026/<p>A <strong>feature store</strong> is a centralized platform that manages, stores, and serves machine learning features for both training and inference. It solves one of the most common pain points in production ML: the gap between how features are computed during experimentation versus how they are served in production. Without a feature store, data science teams often rebuild feature pipelines from scratch for every model, leading to training-serving skew, duplicated effort, and inconsistent results.</p>https://www.pistack.xyz/posts/self-hosted-ml-experiment-tracking-mlflow-clearml-aim-guide-2026/Fri, 17 Apr 2026 00:00:00 +0000https://www.pistack.xyz/posts/self-hosted-ml-experiment-tracking-mlflow-clearml-aim-guide-2026/<p>Machine learning teams generate dozens — sometimes hundreds — of training runs before settling on a production model. Without proper tracking, it becomes nearly impossible to reproduce results, compare hyperparameter configurations, or understand why one model outperformed another. Commercial experiment tracking services lock your data behind paywalls and usage limits. Self-hosted open-source alternatives give you full ownership of your experiment data, unlimited tracking, and deep integration with your existing infrastructure.</p>