As enterprises mature in their appreciation and use of machine learning, deep learning, and AI, a critical question arises: How can they scale and industrialize ML development?

Part of the answer to this question is supporting data scientists and ML engineers with appropriate processes and technology platforms.

To help enterprises understand the platform landscape and attendant issues, TWIML is excited to present our eBooks on emerging enterprise ML & AI platform technologies.

This ebook explores the ways in which tools and platform technologies can support the machine learning workflow. Starting from a look at the platforms built by leading data-first companies (e.g. Facebook, Uber, and Google), we identify the various process disciplines that they embody, and what these say about the landscape of MLOps platforms for the enterprise.
In this ebook, we look at enterprise ML and AI platform needs from the bottom up, with a focus on infrastructure support for data science and machine learning teams. Kubernetes is a strong infrastructure contender for ML/DL workloads, for reasons explored in this ebook.