Snowflake has incorporated NVIDIA’s CUDA-X library into its ML platform, enabling immediate access to GPU-accelerated algorithms for customers. This integration streamlines the entire ML model development lifecycle, allowing data scientists to vastly increase their productivity with core Python libraries without additional coding.
According to Snowflake, as the size of datasets held by companies rapidly expands, the need for efficient cost management and productivity through GPU acceleration is crucial. NVIDIA benchmarks indicate that the A10 GPU can significantly outperform CPUs, with speed improvements up to 200 times in certain algorithms. Users can now leverage libraries like ‘cuML’ and ‘cuDF’ seamlessly within Snowflake ML, saving time in the development cycle of other popular libraries such as ‘scikit-learn’ and ‘HDBSCAN’.
Both companies aim to enhance AI capabilities within their data cloud through this integration. Snowflake is set to strengthen its collaborative efforts, allowing enterprises to harness advanced GPU-accelerated tools for everything from traditional ML model development to deploying enterprise-level large language models (LLMs). This partnership is positioned to drive significant productivity gains in ML endeavors.
👉 Pročitaj original: CIO Magazine
