
The Spell platform streamlines Data Science and Machine Learning for teams of any size. A flexible end-to-end MLOps platform, Spell's platform gives teams the tools to efficiently prepare, train, deploy, and manage machine learning projects. Spell is built for team collaboration, project monitoring, and experiment reproducibility. Streamline DevOps tasks required to manage and scale machine learning projects. Save time on setup and management of ML projects with simple command-line tools and an intuitive web console. Spell takes care of machine management and environment setup giving you more time for experimentation. Teams can access and share the same data, experiments, and results. Spell’s platform is straightforward, making it easy for new hires to get up to speed. Spell was founded in 2017 and is based in New York City.

The Spell platform streamlines Data Science and Machine Learning for teams of any size. A flexible end-to-end MLOps platform, Spell's platform gives teams the tools to efficiently prepare, train, deploy, and manage machine learning projects. Spell is built for team collaboration, project monitoring, and experiment reproducibility. Streamline DevOps tasks required to manage and scale machine learning projects. Save time on setup and management of ML projects with simple command-line tools and an intuitive web console. Spell takes care of machine management and environment setup giving you more time for experimentation. Teams can access and share the same data, experiments, and results. Spell’s platform is straightforward, making it easy for new hires to get up to speed. Spell was founded in 2017 and is based in New York City.
What they do: End-to-end MLOps platform for training, deploying, monitoring, and managing ML/DL projects
Founded / HQ: 2017, New York City
Team size (reported): Approximately 18 employees
Notable funding: $15M Series A announced Jan 17, 2019
Founders / CEO: Founders: Serkan Piantino and Trey Lawrence; Serkan Piantino listed as CEO
MLOps, infrastructure and tooling for training, deploying, and managing machine learning/deep learning projects at scale.
2017
Enterprise MLOps / Machine Learning Infrastructure
$15,000,000
Series A announced Jan 17, 2019
“Backed by venture investors including Eclipse Ventures, Two Sigma Ventures, and Blossom Capital”