50%
“50% savings on cloud costs”
Tal como se publicó en anyscale.com. Capturado por usedby el 7 oct 2026.
Qué pasó
Handshake uses Anyscale's managed Ray platform to train, fine-tune and host LLMs and deep learning recommender models such as graph neural networks and two tower recommenders. These power job matching, recommendations and LLM-generated content for students and employers.
Resumen escrito por usedby a partir de la página de origen, en inglés. Las cifras son de Anyscale y de Handshake, no nuestras.
- 5x“5x faster iteration for AI workloads, accelerating time to market and innovation velocity”
- >50%“10x scalability and >50% cost savings for LLM GPUs”
- 90%“90% higher engagement on jobs, a key business metric”
- 30%“while saving 30% on costs versus comparable A100 workloads”
The team’s velocity has increased significantly with Anyscale, which is essential for lean teams that have to move fast. Anyscale provides a platform that has transformed Handshake’s data scientists into full-stack machine learning engineers with only a lean team of 1-2 ML infrastructure engineers supporting and maintaining it.
Iterating and scaling foundational embedding models (graph neural networks, two tower recommenders) trained over 100M-200M+ interactions was difficult and time-consuming until we migrated our feature producers and training jobs to Anyscale + Ray. Our experiment velocity with deep models and dependencies has 5x’ed while training on more data for cheaper. Anyscale has also enabled net-new real-time inference services and real-time user experiences that data scientists can roll out without waiting on ML Ops experts.
Lo que dice la historia, y lo que verificamos
Comparamos la historia con su página en línea el 7 oct 2026.
- La cifra: 50%VerificadoImpresa palabra por palabra en la página, cerca del nombre de Handshake.
- El pasaje citado arribaVerificadoCopiado palabra por palabra de la página, cerca del nombre de Handshake.
- Handshake usa AnyscaleVerificadoLínea de nivel Confirmado. Última verificación entre todas las fuentes: 7 oct 2026.
- El resultado en síNo verificadoLo citamos; no lo medimos.
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