We work closely with AI teams to ensure the optimum use of compute resource for AI/ML model training and inference. Control costs, remain flexible and guarantee access to the resources you need to train and deploy.
Flexibility
Being unconstrained by cloud provider choice is a significant advantage for an AI company, giving more flexibility in choice of instance type and scale and increasing the speed of machine learning.
We enable AI teams to go multi-cloud from day one, accessing compute from all major providers and automatically provisioning based on price (spot pricing), machine type or even carbon footprint.
This can save up to 90% of costs, due to the irregular workloads and large amounts of data required for models to learn. With the added benefit of accessing free credits from all major providers (up to $100,000 per year per provider).
Increased speed of machine learning through flexibility
Access compute from all major providers and optimal provisioning
Save up to 90% of costs through intelligent provisioning strategy
Accessibility
A clear challenge is accessing the right compute for model development and YellowDog locates all required compute as it becomes available (including GPU, FPGA and AI-specific cores).
Access the right compute for your model anywhere in the world
Work with on-premise, public and hybrid cloud deployments
Event-driven scheduling completes tasks without human interaction
Scalability
To demonstrate the power of our platform, we partnered with AWS to create and then take down one of the world’s biggest supercomputers in the cloud, in under an hour.
This included 1 million vCPUs working within seven minutes, showcasing how rapidly we enable technology companies to develop products and science.
Tell us a bit about your AI/ML application(s) and a member of our team will be in touch shortly.
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