Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Unified Python framework for building high‑performance AI inference APIs
BentoML lets you turn any AI/ML model into a production‑ready REST API with minimal code, automatic Docker packaging, GPU optimization, and seamless deployment to BentoCloud or any container platform.
BentoML is a Python library that streamlines the creation of online serving systems for AI applications. Developers write a small service file, annotate functions with type hints, and instantly obtain a RESTful inference endpoint that works locally and scales to production.
The framework automatically handles dependency management, builds reproducible Bento artifacts, and generates Docker images, eliminating the "dependency hell" often encountered in model serving. Built‑in optimizations such as dynamic batching, model parallelism, and multi‑model pipelines maximize CPU/GPU utilization. Services can be run locally, containerized for any environment, or deployed to BentoCloud for managed scaling and observability. BentoML supports every major ML framework, modality, and custom runtime, allowing teams to integrate bespoke business logic while maintaining a consistent deployment workflow.
Ideal for ML engineers, data scientists, and DevOps teams that need a fast, reliable path from model training to production inference, whether for LLMs, vision models, audio processing, or multimodal pipelines.
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No. BentoML can serve models directly on your machine; Docker is only required for containerized production deployments.
BentoML works with any Python‑based framework—TensorFlow, PyTorch, Transformers, Scikit‑learn, and more—by loading the model in your service code.
Yes. You can push the generated Docker image to any container registry and run it on AWS, GCP, Azure, or on‑premise Kubernetes clusters.
Each built Bento artifact includes the model files and a version tag, enabling reproducible deployments and easy rollback.
No. BentoML collects anonymous usage data by default, but you can opt out with the `--do-not-track` flag or the `BENTOML_DO_NOT_TRACK` environment variable.
Project at a glance
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