Deep learning model development and management drives special requirements, especially around the provisioning of hardware. Spell introduced a machine learning operations (MLOps) platform last week that caters to these very needs.

New York-based startup, Spell, has launched what it calls a cloud-agnostic MLOps platform that is targeted to serve the more complex and unique needs of deep learning using the principles of MLOps used for machine learning.

Spell today unveiled an operations platform that provides the tooling needed to train AI models based on deep learning algorithms.

Unique “Dlops” Software Manages and Automates the Full AI Life Cycle for Enhanced Governance, Time-to-Value, and ROI

Spell’s new cloud-agnostic, end-to-end platform for deep learning tracks, manages, and automates the entire deep learning workflow, from developing and training to deploying and optimizing models at scale. 

Spell.ml develops a machine learning platform for deep learning operations (DLOps) that the company says goes beyond traditional machine learning with its capabilities for preparing, training, deploying and managing the full lifecycle of machine learning and deep learning models.

Spell.ml develops a machine learning platform for deep learning operations (DLOps) that the company says goes beyond traditional machine learning with its capabilities for preparing, training, deploying and managing the full lifecycle of machine learning and deep learning models. Spell.ml says its cloud-agnostic platform can help reduce the costs of deep learning model development.

Spell.ML’s machine learning platform for deep learning operations goes beyond traditional machine learning with its capabilities for preparing, training, deploying and managing the full life cycle of machine learning and deep learning models.