Inspiration
Inspiration
MLOps teams spend 90% of their time on infrastructure setup instead of actual ML research. We saw data scientists struggling with:
- 40% of time lost configuring SageMaker, IAM roles, and S3 buckets
- Decision paralysis choosing from 100+ instance types and 500K+ Hugging Face models
- No automatic budget enforcement leading to cost overruns
- Deep AWS expertise required just to launch a training job
Our vision: What if you could just say "Train a NER model under $10" and have an AI agent handle everything?
What it does
llmops-agent is an intelligent, conversation-driven MLOps platform that automates the complete ML lifecycle through natural language. Give it a single sentence like:
"Train a Named Entity Recognition model on the ciER dataset. Budget: $10, Time: 1 hour, F1 > 85%"
And it:
- ✅ Discovers and validates datasets
- ✅ Selects optimal model architecture
- ✅ Provisions cost-effective GPU infrastructure
- ✅ Launches SageMaker training with LoRA optimization
- ✅ Delivers: F1: 87.3%, Cost: $4.20, Time: 42 minutes
42 minutes from idea to production model. Zero infrastructure management. 58% under budget.
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for Untitled
Built With
- chat-driven
- fast
- mlops
- nvidia-nim-integration
- real-time-streaming
- sagemaker