🚨 Just launched: DataRobot announces Agent Workforce Platform built with @NVIDIA.
✅ Built for enterprise agents
✅ Cloud, on-prem, or hybrid
✅ Full lifecycle control
Read more: https://t.co/CPshkcZkCd
#AI#agenticAI#NVIDIA
Our architecture cements DataRobot's leadership in secure, observable GenAI at scale, leveraging GPU acceleration for large scale inference. Checkout: https://t.co/ww9U58NCFs
If you're passionate about building production GenAI systems that actually work in the enterprise, DM me or check out https://t.co/10Of0VtM2S
Vector Database as a Service - building enterprise-grade RAG pipelines. Recently shipped these features, expanding soon to fine-grained access control for RAG flows:
- Direct Pinecone/Elasticsearch integration maintaining native performance
- Intelligent chunking strategies with configurable overlap and metadata extraction
- Production deployment with automatic drift detection and relevance metrics
- Expanded unstructured data connectors including Native S3/ADLS connectors with chunked streaming
The Agentic AI Platform isn't just another orchestration layer, we built:
- Universal framework abstraction - developers can build with LangChain, CrewAI, or LlamaIndex in VS Code, DataRobot, or notebooks. Our decorators auto-recognize tool/model interrelations.
- Full OTEL-compliant distributed tracing - every tool call, every LLM interaction, with latency breakdowns
- Advanced debugging with side-by-side agent execution comparison and state inspection
- Synthetic test generation using grounding data + configurable "LLM as Judge"
LLM Gateway - solving the multi-cloud LLM challenge:
- Single OpenAI-compatible endpoint abstracting LLMs across all major providers
- Direct integration with provider auth - no credential management needed
- Configurable parameters (temperature, max_tokens) passed through to providers
- ClientId metering for usage tracking (playground, custom-model, moderations)
- Dynamic catalog API to discover supported models across providers
DataRobot was a pioneer in AutoML with predictive models, that DNA seeded the idea to build Syftr at DataRobot. Special shoutout @debadeepta for leading Syftr and giving life to this idea. Looking forward to get Syftr natively in DataRobot.
Building an agent or RAG system with high accuracy and low cost and latency can be a daunting problem. Between the choice of language models, prompts, depth of reasoning, text splitters, retrievers etc., the options to explore can be innumerous. Hence we build Syftr at @DataRobot
The best part is syftr is fully open-source. Clone the repo and try it out yourself. It can be configured to support various LLMs, prompt strategies, RAG configs etc. Check it out yourself here
https://t.co/FIBMrGWVe0
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