TensorStax provides AI agents that automate data pipeline work: discovering sources, ingesting and transforming data, validating quality, and surfacing observability. Embedded in ETL workflows, these agents accelerate delivery, cut manual toil, and make pipelines production ready, trustworthy.
Feature stores and real-time pipelines are essential for production ML, ensuring consistent, low-latency features. Open-source tools like Feast, Hopsworks, and Feathr provide scalable, flexible, and observable pipelines, enabling teams to deploy robust, reliable machine learning at scale.
DSPy is an open-source framework that lets developers program large language models with structured, modular code instead of relying on prompts. It enables scalable, self-optimizing AI pipelines, offering reliability, flexibility, and faster iteration for complex AI workflows.
