Join Dataloop at GTC 2025, NVIDIA’s flagship conference, from March 17–21 at the San Jose Convention Center. Visit Dataloop’s booth #2009 to connect with our experts and executives and learn how to simplify AI workflows. Discover real-world AI pipeline solutions through live demonstrations, showcasing Dataloop’s advanced platform and its integration with NVIDIA technology to enhance model development and high-performance inference.
Explore Live Demonstrations to Simplify and Scale Your AI Workflows
Agentic AI Orchestration platform Powered by NVIDIA NIM™
Fine-Tuning Embedding Models
Retrieval-Augmented Generation (RAG) Workflows Powered by NVIDIA NIM™
Automated Data Preparation Pipelines Powered by NVIDIA NIM™
Vision-Language Model (VLM) Agents
1. Agentic AI Orchestration Platform Powered by NVIDIA NIM™
Learn how to orchestrate and manage complex multimodal AI workflows with Dataloop’s AI platform, integrated with NVIDIA NeMo™ and NVIDIA NIM™. See how embedding NVIDIA NIM™ into Dataloop’s orchestration layer minimizes setup and configuration overhead, enabling immediate deployment of GPU-accelerated inference pipelines. Ensure high-performance, multimodal data processing at scale while automating model inference, scaling deployment, and accelerating real-time data processing across cloud, on-premises, and hybrid environments with enterprise-grade security
Key Values:
Automates data processing for unstructured multimodal data, including text, images, and videos, leveraging NVIDIA’s powerful inference capabilities.
Builds autonomous and adaptable AI agents through generative AI and RLHF for continuous learning.
Simplifies the AI lifecycle from fine-tuning models to real-time decision-making and workflow orchestration.
Applications: Ideal for real-time decision-making, automated processes, and adaptive AI solutions across diverse datasets.
2. Fine-Tuning Embedding Models
Refine embeddings for domain-specific applications like search optimization, similarity detection, and personalized recommendations.
Customizes embeddings for unstructured multimodal data, ensuring high-quality feature representation for text and images.
Leverages RLHF to iteratively improve embedding accuracy, precision, and relevance.
Streamlines workflows for deploying tailored AI models efficiently across diverse industries.
Applications: Perfect for search systems, recommendation engines, and workflows requiring domain-specific optimization.
3. Retrieval-Augmented Generation (RAG) Workflows Powered by NVIDIA NIM™
Enhance generative AI outputs by integrating real-time data retrieval into workflows with NVIDIA NIM™, enabling context-aware and actionable results through GPU-accelerated processing.
Dynamically integrates unstructured multimodal data into generative AI workflows for accurate insights.
Automates RAG workflow deployment and refines processes through RLHF.
Combines retrieval systems with generative AI to deliver user-specific outputs at scale.
Applications: Perfect for chatbots, knowledge management, and AI tools requiring context-aware, real-time outputs.
4. Automated Data Preparation Pipelines Powered by NVIDIA NIM™
Convert raw, unstructured data into structured datasets ready for AI training using Dataloop’s orchestration platform, powered by NVIDIA NIM™ and NeMo, for scalable, high-performance processing.
Automates tasks such as data processing, entity extraction, and feature generation for unstructured multimodal data.
Handles diverse data types within a unified pipeline, enabling efficient scaling.
Incorporates RLHF to refine datasets and improve AI model performance iteratively.
Applications: Ideal for industries like autonomous vehicles, retail optimization, and agriculture, where large-scale datasets are essential for AI training.
5. Vision-Language Model (VLM) Agents
Learn how VLM agents analyze and process multimodal data to streamline workflows using NVIDIA’s NeMo framework and the cutting-edge NVIDIA Cosmos Nemotron 34B. This advanced vision-language model is engineered to query and summarize images and videos from both physical and virtual environments, transforming complex inputs into actionable insights.
Key Values:
Processes and aligns text and visual data, enabling tasks like image-to-text inference, semantic search, and contextual data enrichment.
Automates multimodal workflows, reducing manual effort and streamlining AI-driven decision-making
Leverages real-time feedback mechanisms to continuously refine outputs for enhanced accuracy and adaptability.
Applications: Perfect for industries requiring multimodal data insights, such as media analysis, automated content creation, and retail catalog optimization.
Stop by Booth #2009
Let’s talk about how Dataloop’s platform can help you scale your AI projects and adapt to evolving data needs.
