The value you can provide with AI is limited by how fast you iterate through ideas. Things like clunky infrastructure and slow data loading only distract from your job. We created Grid to fix that. Whether you are building a production-grade AI pipeline, doing drug discovery, or pushing the SOTA of AI research, Grid 10Xs your iteration speed by scaling on the cloud from your laptop.
Using Grid in the Real World
Navaeh and Alaia are recent MS in Data Science grads. At their startup, they are building models to detect what topics their company should write about to maximize their engagement on social media. They use Grid because they want to spend their time going through ideas and not on managing infrastructure. Their startup depends on how fast they can get to a working prototype of their idea, and a single GPU on their desktop just won’t cut it. With Grid, they take 3 months worth of ideas in a few days.
Thu is a professor at a leading university research lab for breast cancer detection. Her 15 Ph.D. students use Grid to focus on iterating through their research ideas instead of learning about engineering. Their ablation/idea backlog will take them 3 months on their local DGX-1 machines. By using Grid, they can condense this backlog into a few days.
Evan is a Data Scientist at a Fintech company. His main focus is to build models to detect fraudulent credit card transactions using deep learning. He needs to access his data safely and do large-scale training to experiment on different models quickly. Thanks to Grid, he can experiment from his laptop using powerful jupyterlab instances while sending hundreds of jobs using one line of code. Grid has sped up his workflow from weeks to days.
Nova works at a streaming company where she manages the ML team. They need to create a service that automatically skips TV show intro’s for the product they are building. To do this requires them to process large amounts of data. Their cloud provider does not allow them to have enough access to GPUs. Using Grid, they can launch multiple experiments in parallel, each using hundreds of GPUs concurrently. This allows them to iterate faster and ship their product in a matter of weeks rather than months.