Resources

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. Hello

RAG: Beyond the Chatbot

Retrieval-augmented generation promised to bring ChatGPT's magic to enterprise data. But while organizations rushed to build chatbots, they often struggled to deliver real business value. This comprehensive guide reveals RAG's full potential beyond conversational interfaces, showing how integration with existing tools and processes can unlock powerful enterprise-wide capabilities. Drawing from concrete examples and implementation experience, we provide both strategic vision and practical guidance for deploying RAG in ways that drive meaningful outcomes. Whether you're just starting with RAG or looking to expand, this report offers a clear roadmap for unlocking the technology's true value.

The Definitive Guide to Machine Learning Platforms

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.

Kubernetes for MLOps

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.