Healthcare R&D

HDS_Healthcare-03 OVHcloud

Healthcare startups use OVHcloud AI Training to control costs & grow fast

ML training for healthcare R&D

Controlling the costs of an AI powered R&D lab is a key factor in the success of any start-up or innovation-driven organization. Even early-stage startups, with time-to-market constraints and limited data resources, can help clients scale their workload through the power of AI. 

For example, in medical research, image processing for MRI scans can be resource intensive, as it requires splitting and processing large files containing several gigabytes. This process can quickly translate into soaring costs.

The OVHcloud AI Training solution responds to the needs of AI-powered research start-ups by enabling the end-to-end development of AI applications. This allows start-ups to increase their speed to market, while minimizing the cost of development.

Why choose OVHcloud AI Training?

OVHcloud AI Training empowers your R&D teams with both training-as-a-service, and GPU-as-a-service. From the on-boarding of data, to experimentation; the services include labelling, training and the comparison of models up to their deployment.

OVHcloud AI Training enables all start-up team members to collaborate easily, so they can identify patterns of interest, track progress, and capture learnings.

The solution will enable a fast and cost-effective method for labelling data. In the case of MRI scans, healthcare experts participating in the labelling process can focus their efforts on images that help model performance. This reduces costs, and the time it takes to develop AI solutions. 

Benefit 1: Accelerate your time to market

Experiment and development times are compressed to help you build a competitive advantage in fast-paced innovation markets. 

Benefit 2: Stick to/limit your allocated budget

By streamlining your AI training operations, you can make the most out of your existing budget while ensuring optimal performance.

Benefit 3: Scale fast and grow

Leverage the flexibility to scale resource-intensive activity; such as image processing, and AI modelling workloads.