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Spell, the best machine learning infrastructure platform and Weights & Biases, the best machine learning visualization platform, now work seamlessly together to further streamline the deep learning development process. 

Spell and Weights & Biases have partnered up so teams can seamlessly run deep learning experiments on any hardware, and automatically view and analyze results in the Weights & Biases interface, streamlining deep learning development. Together, the platforms enable teams to overcome the hurdles of setting up and managing infrastructure and environments, tracking, visualizing, and reporting on deep learning projects. 


Spell and Weights & Biases are building developer tools for machine learning practitioners. Both teams have a shared goal: make training, optimizing, and deploying machine learning models accessible and approachable to everyone. This product integration has the potential to increase productivity and efficiency for users by providing teams seamless integration from setup and training with Spell’s infrastructure management platform to automatically tracking, visualizing, and reporting metrics with Weights & Biases to deploying models.  


Spell is a cloud platform for building and managing machine learning and deep learning projects. Spell takes care of the setup and management of the infrastructure required for deep learning projects, which makes deep learning projects easier to start, faster to get results, more organized, safer, and simpler than managing infrastructure on your own.

Weights & Biases is a cloud platform that allows users to track the development and experimentation of deep learning models at scale. With their lightweight python library users can save hyperparameters, optimize model accuracy, and visualize their metrics. The platform makes it easy for developers to iterate faster, collaborate, and understand what is happening within their deep neural networks. 


Building top-of-the-line tools for deep learning is at the heart of both companies.  We know our partnership will further the mission of making AI and deep learning more accessible to everyone.