Manual fixes for node failures held back training. Automated recovery now drives 4x faster customer deployments.
An enterprise software company provides a full-stack generative AI platform that enables customers to build and deploy proprietary models within their own security perimeters.
Pretraining and fine-tuning domain-specific large language models required massive computational resources, but the company lacked a dedicated...
“When we first started, we wanted to see how this solution would grow and adapt to our needs, and we couldn’t be happier with the support we received from AWS. Getting the support we needed in a timely manner helped us gain confidence in the Amazon SageMaker HyperPod service and sped up our AI research work.”
Enterprise generative AI platform for regulated industries.
Cloud computing platform and on-demand infrastructure services.
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Hardware failures interrupted training runs. Automated recovery cut model training time by 40%.
Standard inference stalled at 1k tokens/sec. A custom engine hit 10k/sec, cutting 20-second refactors to under 400ms.
Manual prompt tuning couldn't keep pace. Automated feedback loops now refine models using real-time user comments.
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.