Spell is a powerful platform for building and managing machine learning projects. Spell takes care of infrastructure, making machine learning projects easier to start, faster to get results, more organized and safer than managing infrastructure on your own.
Intuitive tools and simple commands allow you to quickly get started and immediately see the productivity benefits of having infinite computing capacity at your fingertips.
Explore your data with Jupyter notebooks, train models on powerful GPUs, create APIs, and automate your entire workflow, Spell makes setting up ML pipelines easy.
Run your experiments and models on your own AWS or Google cloud instance, automatically generate records, and keep your data in one place.
Spell is built for Team collaboration, project monitoring, and experiment reproduction. The platform’s Jupyter workspaces, datasets, and resources are straightforward and accessible. Spell’s clear and concise flow also makes it easy to onboard new hires and get them up and running quickly.
Spell’s white glove service features on-premise deployment and real-time Slack support. Companies can work on their specific operations. The platform integrates with Single-Sign-on systems, internal data stores, and governance systems. This premium service provides everything necessary for clients to be successful in their Machine Learning and Deep Learning endeavors.
End-to-end tools that make your team more productive and get faster results.
Turn any model into a REST API with a single command, and manage performance and scaling in our web dashboard.
Automate, monitor, and optimize the different stages of your machine learning pipeline.
Get easy performance wins with our one-command hyperparameter search.
Use Jupyter Notebooks or Jupyter Labs powered by Spell's GPUs, and easily collaborate on a notebook within an organization.
View queued and running jobs across your organization and better allocate capacity to meet your organization’s unique needs.
Spell’s cluster management interface allows you to manage your own cloud and spin up and down instances as needed.
The Core Capability That Enterprises Need To Deploy, Monitor, And Govern Machine Learning Models
Model operations (ModelOps) is a must-have capability to operationalize AI at scale. Also known as machine learning operations (MLOps), ModelOps comprises tools, technologies, and practices to enable organizations to deploy, monitor, and govern machine learning models and other analytical models in production applications.
Download your complimentary copy of this new report today to learn more about why ModelOps is key to bringing machine learning projects to production. This is a must-read for machine learning professionals.
See some of their testimonials here
I had the opportunity to work with Spell on a challenge for Omdena to preprocessing our datasets. This was a great experience because on my laptop this process took many hours while on Spell it was much faster and my laptop didn't freeze. Additionally, Spell had logs where I can monitor my process, kill the run if it is necessary, and I can see how long the run is so I can monitor performance in my code.
Spell is a collaborative platform that lets anyone run machine learning experiments.
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