mlflow and mlox
MLflow provides a comprehensive MLOps platform for experiment tracking, model registry, and production monitoring, while MLox appears to be an infrastructure layer for deploying and managing AI workloads—making them complements that could be used together in an end-to-end ML pipeline.
About mlflow
mlflow/mlflow
The open source AI engineering platform. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI agents, LLM applications, and ML models while controlling costs and managing access to models and data.
MLflow provides distributed tracing built on OpenTelemetry with automatic instrumentation for 60+ frameworks, enabling cross-language support (Python, TypeScript, Java). Beyond tracing, it includes a unified AI Gateway for multi-provider LLM routing with rate limiting and fallbacks, plus integrated prompt versioning and optimization using state-of-the-art algorithms. For traditional ML, it offers experiment tracking, model registry, and multi-platform deployment (Kubernetes, SageMaker, Azure ML).
About mlox
BusySloths/mlox
Sovereign AI Infrastructure. Open by Design. Slothfully Simple.
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