Spell is the MLOps platform built to meet the unique challenges of operationalizing deep learning at scale. For engineers, it eliminates drudgery and enhances collaboration. For managers, it provides real-time project visibility and accountability. And for stakeholders, it reduces cost and shortens time to value.
Spell DLOps is comprehensive and inclusive, meeting the needs of the engineer, the team, and the enterprise for effective development, deployment, and management of deep learning models. Spell operates on public, private, and hybrid clouds, or on dedicated on-premises compute infrastructure. It easily integrates with existing workflows, frameworks, infrastructure, and datastores. Spell doesn’t force users to learn new deep learning tools and technologies; it makes existing ones easier to use.
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.
Speed up development, simplify deployment, and automate cloud compute
Easily distribute your code to run projects in parallel. Built-in hyperparameter optimization tools, and integrations with Tensorboard and Weights & Biases help improve models up to 10x faster.
Deploy easily to your private cloud and quickly start machine learning projects. Bring your own AWS or GCP credits, keep data in your S3 or Google Cloud buckets, and deploy models within your private cloud infrastructure.
Deploy models in one-click on industrial-grade, auto-scaling, Kubernetes-based infrastructure. Easily manage the model life cycle, model versions, and performance results of inference.
Take control of your projects from start to finish. Our Workflow API and Metrics API allow you to automate key stages in your ML pipeline. Create charts to track the performance of your code.
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