AI Deployment & MLOps AI Tools
Open-source tools for packaging, serving, versioning, and deploying machine learning models in production.
Getting a trained model out of a notebook and into something that serves requests reliably is a different discipline from training it. Platform and ML engineers reach for this category when there is a working checkpoint and no path to production behind it. The field in 2026 splits into roughly three camps. Kubernetes-native control planes like KServe, Kubeflow, and Seldon Core assume a cluster exists and pay that back with autoscaling, canary rollouts, and multi-model routing. Packaging-first tools like BentoML, Cog, and Truss invert the order, treating the model and its dependencies as the shippable artifact and staying agnostic about where the container lands. A third camp handles capacity: SkyPilot, dstack, and Metaflow chase GPUs across clouds, Kubernetes, and bare metal. The tradeoff is operational surface area against ceiling. Packaging tools reach a first endpoint in an afternoon and run out of room; the Kubernetes stack has almost no ceiling and needs someone fluent in CRDs. A sensible first pass is BentoML for the initial endpoint, DVC or Aim to record which checkpoint produced which numbers, and Ray Serve once one model becomes a pipeline of several. The license and the commit log are worth checking first. Seldon Core moved its v2 line to the Business Source License, so current releases are not open source for commercial serving, while v1 stays Apache 2.0. MLEM, listed here, has been archived by its maintainers. Both are easy to adopt on the strength of a README written in a different year.