Grid.ai platform enables researchers and data scientists to train AI models on the cloud

Grid.ai announced the general availability of Grid, a new platform that enables researchers and data scientists to train AI models on the cloud at scale, from a laptop with zero code changes.

The availability of Grid enables AI researchers, machine learning engineers, and data scientists to do development and training at scale without requiring advanced skills in machine learning engineering or MLOps engineering.

These practitioners can run into challenges when scaling their AI workloads because the sophistication needed to train and deploy at scale requires complex infrastructure, making the barrier to entry very high.

Practitioners can now focus on delivering value with AI as opposed to dealing with the infrastructure challenges needed to leverage deep learning at scale.

Grid.ai solves this challenge by creating an easy-to-use interface for training models on the cloud.

“We’re excited to lead this next wave of technologies to eliminate complex infrastructure from AI workflows so practitioners can focus on what they do best which is iterate through their ideas fast so they can bring value to their companies and research labs even faster,” said William Falcon, CEO, and Founder of Grid.ai.

“With Grid, practitioners no longer need to be expert engineers for building AI systems at scale.”

The benefits of Grid

• Accelerates time to production. Practitioners can iterate through hundreds of ideas in days not months. This dramatically shortens the time to production.
• Web application customized for data scientists with a flexible command line interface.
• Saves money by avoiding wasted compute spend on infrastructure setup. Every dollar spent goes towards compute – not infrastructure.
• Optimizes giant datasets to work at the scale needed for production and cutting-edge research workloads.
• Computes costs in real-time to make it easy to quantify the R&D efforts of any AI project.
• Provides access to Jupyter notebooks on any configuration hardware at scale.

“I really like the fact that it is seamless for me to train my models from my local computer to Grid and I don’t have to worry how it is done, it just works,” explained Jesse Perla, Assistant Professor of Economics at the University of British Columbia.

“I can now focus my time on the theoretical side of research trying new ideas on the cloud faster thanks to Grid.”