MLOps Platforms

Machine learning platform for managing model training, experimentation, and deployment at scale on Kubernetes.

Open-source library for estimating ML model performance post-deployment without ground truth labels.

Open-source LLM evaluation and observability platform from Comet.

AI observability platform providing data and model monitoring with drift detection and anomaly identification.

Open-source vision AI platform with annotation, training, and deployment.

Open-source ML toolkit for Kubernetes enabling portable and scalable ML workflows on cloud infrastructure.

Open-source Python framework for creating reproducible and modular data science and ML pipeline code.

Open-source platform for managing the ML lifecycle including experimentation, reproducibility, and model deployment.

Mobile-first ML platform for developers building AI-powered iOS and Android apps.

AI observability platform for monitoring, explaining, and analyzing ML model predictions in production.

Open-source LLM observability framework using OpenTelemetry for traces and metrics.

Open-source ML monitoring tool for evaluating, testing, and monitoring data and model quality in production.

Experiment tracking and model registry for ML teams logging metrics, parameters, and artifacts across training runs.

Open-source LLM evaluation and observability tool for production AI pipelines.

Feature store and ML platform providing feature engineering, model training, and inference pipeline management.

Enterprise MLOps platform built on Flyte for orchestrating ML and data workflows at scale.

Open-source feature store for ML serving production features to models with offline and online storage.

Open-source ML pipeline framework combining code-first workflows with production deployment.