Local computers bottlenecked 50 researchers. Running 100 simultaneous cloud experiments cut voice model training from days to hours.
A generative AI platform serving 50,000 customers saw its user base expand by 456%, creating terabytes of training data every week.
Training complex models on local computers became inefficient for 50 researchers who needed to run experiments 24/7. This on-premises bottleneck...
“AWS has been embedded in our company from the beginning. We aim to give more people access to video creation anywhere in the world, and we wanted to build for scale and self-service in the cloud. AWS provides a wealth of services that we wanted to adopt, and it has key initiatives for startups.”
AI video generation platform using synthetic avatars and text-to-speech.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Researchers were stuck maintaining hardware. Moving to managed infrastructure freed them to build and cut training costs by 90%.
Video creation took weeks. Now, GenAI on specialized silicon renders enterprise-grade videos in 30 seconds.
CTechnical lingo and bad mics forced 13-hour edits. AI trained on company terms and stripped reverb, cutting production to 4 hours.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.