Baseten
By Baseten
Baseten is a model inference platform for packaging, deploying, and scaling machine learning models — including large language models — as production-ready API endpoints.
Definition
Baseten is a model inference platform for packaging, deploying, and scaling machine learning models — including large language models — as production-ready API endpoints.
Overview
Baseten focuses on the deployment side of the ML lifecycle: teams package a model (often using Baseten's open-source Truss packaging format) and Baseten handles GPU provisioning, autoscaling, and exposing it as a callable API endpoint. This targets a common pain point in machine learning projects — a model that works in a notebook using frameworks like PyTorch or a Hugging Face checkpoint but needs reliable, low-latency infrastructure to serve real traffic. Because GPU inference workloads have distinctive cost and cold-start characteristics compared to typical web services, platforms like Baseten compete with cloud-native offerings such as Amazon SageMaker and self-managed Kubernetes deployments by abstracting away cluster and autoscaling configuration. It is commonly used for serving generative AI models — LLMs, embedding models, and image generation — which is also the focus of SkillVeris's MLOps & Model Deployment course.
Key Features
- Autoscaling GPU infrastructure for model inference
- Model packaging via the open-source Truss format
- Automatic generation of production API endpoints
- Support for popular ML frameworks and Hugging Face model checkpoints
- Monitoring and observability for deployed models
- Options for private/dedicated deployment environments