How should you organize an ML team? Do centralized data teams work?

In this article, David Hershey, solutions architect at Tecton, describes a common pattern we’ve seen across hundreds of companies using machine learning: centralized data teams don’t work. … Read More

Embeddings have proven to be some of the most important features used in machine learning, enabling ML algorithms to learn efficient representations of complex data. Many embeddings,  in particular embeddings of audio, text or images, are computed with complex (and … Read More

As more and more teams seek to institutionalize machine learning, we’ve seen a huge rise in ML Platform Teams who are responsible for building or buying the tools needed to enable practitioners to efficiently build production ML systems. Almost every … Read More

Amazon recently introduced SageMaker Feature Store, recognizing that one of the largest challenges facing data science teams is building and serving high-quality machine learning features both in training and in production. The team at Tecton could not agree more with … Read More