Building intelligent machines requires intensive processing of large amounts of high dimensional data such as images, video and audio.
Eventual enables teams to run large scale processing on their data without having to think about cloud infrastructure.
Eventual is simple. We make it easy for developers to write cloud native software that scales.
Eventual provides commonly used modules for ML applications right out-of-the-box.
Eventual is built with cloud-agnostic tooling and can deploy directly into your organization’s cloud.
Your code runs exactly the same way locally as it does in the cloud.
We make it easy to go from functions on your laptop to production pipelines in seconds.
Choose from our marketplace of DataApps or build your own to compose complex data processing applications such as:
Eventual pipelines can trigger automatically, enabling a Continuous Delivery of data as it enters your system.
Jay graduated from Cornell University where he did research in Machine Learning and Computational Biology at the Yu and Danko labs. He is from Singapore, and was a tank platoon commander in the Singapore army as well as the Head of Talent Acquisition at Shopback. Jay was the founding engineer of the Machine Learning (ML) platform team at Freenome, building out a platform to detect colorectal cancer from genomic data, and later joined Lyft Level 5 as a senior engineer focusing on ML infrastructure for distributed deep learning.