Spell

Founded 2017

Run machine learning experiments and train models on cloud hardware

You can use Spell to prepare, train, and deploy machine learning models on cloud infrastructure. It lets teams run experiments on GPUs and TPUs without managing complex server clusters. Engineers can track model versions, automate training pipelines, and organize datasets collaboratively. It provides command-line tools and web dashboards to monitor resource usage, logs, and experiment metrics. Teams can also deploy trained models as scalable API endpoints, integrating existing code repositories with automated workflows to speed up modern deep learning development cycles easily.

What Spell does

Launch distributed training jobs from the command lineTrack model training loss and accuracy metricsServe machine learning models behind secure API endpointsMount large training datasets directly to compute instancesSchedule recurring machine learning pipeline workflowsManage cloud GPU resource allocation across projectsReproduce historical training runs with exact environment configsOne-command execution of model training on cloud GPUsAutomated hyperparameter search and distributed trainingInteractive hosted Jupyter notebooks connected to cloud computeIntegrated metrics tracking and visualization for training runsDataset mounting and caching across different compute instancesREST API generation for real-time model servingVersion control integration for code, models, and environments