Accelerated Learning on a Powerful Platform
An earlier version of this page mentions Content Engine, now known as Growth Engine Inc. To see the most up-to-date version, visit: http://join.spell.ml/growth-engine-case-study
- Founded in 2020
- Headquartered in Houston, TX
- Company size — 7 people
- Uses TensorFlow and PyTorch and a range of Natural Language Processing frameworks
Growth Engine Inc. is currently building an Adaptive Training Platform that automates internal and external training for enterprises. By leveraging Machine Learning and Natural Language Processing, they can generate curricula that constantly adapts to learners, as well as content for ongoing training of team members and clients. This saves companies time in managing training materials while ensuring that training materials are always up to date, leading to highly scalable teams and better educated customers.
Prior to using Spell, the team at Growth Engine used custom Notebooks to work on Natural Language Processing experiments, but lacked a well-ordered approach for tracking and organizing experiments. This led to disjointed workflows and high costs to train their machine learning models. As a result, their team decided to use Spell’s platform to move their model training to AWS and GCP, allowing them to scale up to large compute requirements and develop a truly organized approach to running experiments.
Natural language models often require massive amounts of memory and compute power, and thus can greatly benefit from a powerful platform that helps operationalize workflow and regulate costs. Upon adopting the Spell platform to run their Natural Language Processing models, the Growth Engine team was able to use their existing AWS and GCP credits, helping offset potentially high expenses from training these NLP models.
The Growth Engine team found Spell to be straightforward, streamlined, and easy for onboarding. With an intuitive UI side and CLI, Spell is ideal for creating new workspaces, runs, and staying organized via the integrated filesystem.
For moving to larger scale environments, Growth Engine relies on Spell as the only option for them that truly works. Because some of their models have massive memory and compute requirements, moving them to the cloud is the only practical way for them to run experiments. With Spell, Growth Engine now seamlessly executes their heavy NLP models to the production and commercialization phase on a powerful platform.
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