Machine Learning in Research & Higher Education

Researchers, professors, and students are perennially distracted by infrastructure and tooling bottlenecks.  Gradient delivers a fully-managed research environment which enables rich collaboration, faster iteration, and enhanced freedom and control.

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AI in Research & Higher Education

Helping deliver more breakthroughs with AI technology. Gradient is partnering with some of the most respected research institutions and universities to help practitioners develop groundbreaking machine learning models in less time. From University of Chicago to MOOCs like Fast.ai, aspiring and professional data scientists are building the future on Gradient.

Discover the benefits of AI in research and higher education.

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Challenges

Insufficient access to scalable compute resources

Gaining access to compute resources and orchestrating workloads requires extensive experience in tooling that becomes a costly distraction for data scientists and ML engineers. Infrastructure bottlenecks reduce velocity and precision and increase model ops friction, time to market, and operational risk.

Lack of standardization, process, & centralized hub

Collaboration among distributed research teams without a unified tool is a liability. Workflows that lack a standardized process and a unified hub lead to re-work and hinders the ability for data scientists to find, understand, build-on, and contribute to the various models in R+D and production.

Consumed by menial & redundant tasks

Data scientists and machine learning engineers spend roughly 25% of their time developing models. This means that 75% of their time is spent on costly distractions related to tooling and infrastructure. A end-to-end ML pipeline enables a rapid model delivery cycle without the need to perform cumbersome routine tasks.

Gradient can help

Conduct AI without bottlenecks

Experiment faster and deliver more breakthroughs. Remove DevOps pain with independent access to compute. Use the right tools for the job. Share and repurpose work across the team. More easily get models from development into production.

Build a modern AI practice

Create an organizational capability out of machine learning. Gain visibility into work happening across the organization. Accelerate the data machine learning management lifecycle. Reduce regulatory and operational risk while maintaining and updating models more frequently, with greater precision.

Become your unified AI hub

Support data science without sacrificing governance or security. Future-proof your machine learning stack. Gain transparency with complete model reproducibility. Offer a centralized platform for collaboration. Reduce model ops friction for rapid model delivery and iteration.

AI Applications

High Value Use Cases in Research & Higher Education

  • Workshops
  • Research labs
  • Online classes & MOOCs
  • Library and lab deployments
  • Individual and group coursework
  • Project submissions
  • Hackathons & meetups

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