Every engineering team’s roadmap has included a task to “improve their product with machine learning”. The challenge: most organizations lack sufficient data science resources to rapidly build custom models in-house, leaving engineering teams roadblocked on their ML projects and the OKR is pushed back another quarter.
A new generation of declarative machine learning tools—built on foundations pioneered at Uber, Apple, and Meta—changes this dynamic by making ML accessible to engineers and developers. Declarative ML systems simplify model building with a config-driven approach rooted in engineering best practices like automation and reusability, in a similar way that Kubernetes revolutionized managing infrastructure. With these capabilities, developers can build powerful production-grade ML systems in minutes.
Read this ebook to learn:
- About declarative ML systems, incl. open-source Ludwig from Uber
- How to build state-of-the-art machine learning and deep learning models in less than 15 lines of a YAML
- How to get started with open-source Ludwig and Predibase the leading declarative ML platform