Sematic + Ray: The Best of Orchestration and Distributed Compute at your Fingertips

Finding the right tools for machine learning (ML) infrastructure can be challenging due to the plethora of available options, but some combinations, like numpy and pandas, or Sematic and Ray, work seamlessly together to enhance efficiency. Sematic aims to simplify ML workflows by providing features like lineage tracking and reproducibility, and when integrated with Ray, it allows for efficient scaling of data processing and distributed computing. Ray is an open-source framework that facilitates scaling AI and Python workloads across clusters, complemented by native libraries and integrations with popular ML tools. The synergy between Sematic and Ray enables users to create end-to-end ML pipelines with minimal development overhead, leveraging Ray's distributed computing capabilities for tasks such as distributed training and hyperparameter tuning. The integration ensures that the same code and dependencies are maintained across nodes, simplifying execution both locally and in the cloud, while Sematic's Ray integration, part of its Enterprise Edition, provides a robust platform for advanced ML projects.