July 2022 Summaries

The machine learning flywheel is a crucial concept in operationalizing machine learning, which involves creating a feedback loop between decision-making, data collection, organization, and learning. The flywheel consists of four stages: Decide, Collect, Organize, and Learn, which mirror the human experience of making decisions, observing results, combining knowledge, and updating understanding. Successful teams intentionally build tools to manage the entire ML lifecycle, including ownership, agency, and clear dependencies. However, building a great machine learning flywheel is challenging due to the diverse number of tools on the market, lack of interoperability, and need for simple abstractions. Data flows are at the core of the ML flywheel, and managing them can be hard. Feature platforms like Tecton have had an impact by solving part of the top path but leaving out the Collect and Organize stages. To extend data management to the entire ML flywheel, teams should close the loop, establish a unified flywheel data model, and support use-case-specific architectures. A unified ML flywheel can achieve reliable, repeatable, and accurate decision APIs for products, supporting ML product teams using fewer resources and making iteration, building, and deployment faster.

Tecton has raised $100M in Series C funding, bringing its total funding to $160M, and is now positioned to help enterprises integrate real-time predictive applications into their everyday operations. The company's feature platform aims to support all capabilities of operational ML dataflow model, including management of training datasets, serving architectures, event logging, label and ground-truth management, compliance, and monitoring. Tecton has gained significant traction since its founding in 2019, with a five-fold increase in customer base and nearly three-fold growth in ARR over the past year. The company's product roadmap includes integrating with Snowflake, Redis, and Databricks, as well as releasing a managed online store and guided onboarding features to simplify the feature store experience for customers. With this funding, Tecton plans to continue its partnerships and integrations, aiming to make operational ML simpler, more reliable, and achievable for ML teams around the world.

A feature platform is a system that orchestrates existing data infrastructure to continuously transform, store, and serve data for operational machine learning applications. It enables users to define features as code, manage them as code assets, and run data pipelines to compute features automatically. Feature platforms solve the engineering challenges of putting features into production by abstracting away complexity and enabling fast iteration cycles in the development process. They provide a standardized way of defining and reusing features across teams and models, making it easier for ML teams to quickly produce new features and power multiple use cases. By managing data pipelines, feature stores, and monitoring capabilities, feature platforms make operational machine learning applications more reliable, scalable, and maintainable.