Product
About
Rachel Rapp is part of Baseten's product team. With a background in applied machine learning research, she helps organizations navigate the complexities of deploying high-performance machine learning models in production. Originally from a small town in Michigan, nowadays you can find her in Germany with her husband and crew of former-Romanian-street pets.
Baseten Embeddings Inference (BEI) delivers 2x higher throughput and 10% lower latency for production embedding, reranker and classification models at scale.
Baseten Chains delivers ultra-low-latency compound AI at scale, with custom hardware per model and simplified model orchestration.
We added three new observability features for improved monitoring and debugging: an activity log, LLM metrics, and customizable metrics dashboards.
Our new Speculative Decoding integration can cut latency in half for production LLM workloads.
At Baseten, we've built the most performant (1000x real-time factor), accurate, and cost-efficient speech-to-text pipeline for production AI audio transcription
Deploy production-ready model servers on Baseten directly from any Docker image using just a YAML file.
Test and deploy ML models reliably with production-ready custom environments, persistent endpoints, and seamless CI/CD.
Our canary deployments feature lets you roll out new model deployments with minimal risk to your end-user experience.
