Dataloop AI

AI is revolutionizing agriculture through computer vision and machine learning, enabling unprecedented precision in crop disease detection and management. However, building reliable AI systems in agriculture presents unique challenges around data quality, infrastructure, and real-world deployment. Join leaders from Syngenta and Taranis as they discuss the practical realities of implementing AI in agriculture, from building robust data pipelines to deploying models in low-connectivity environments. Learn how they're combining synthetic data, foundation models, and edge computing to create scalable solutions for farmers worldwide. In this session you're going to learn:  ✅ Data Quality: How to build and validate high-quality training datasets that represent real-world agricultural conditions ✅ Pipeline Architecture: Best practices for orchestrating ML workflows that can handle varied data sources and connectivity constraints ✅ Synthetic Data: Strategies for blending synthetic and real data to improve model performance while maintaining authenticity ✅ Edge Deployment: Approaches to deploying AI models in resource-constrained environments while ensuring reliability ✅ Future Technologies: How multimodal AI and foundation models are transforming agricultural decision-making

“I would press the easy button––and I would use a platform like Dataloop again.” NVIDIA’s Director of DGX Product Architecture, Michael Balint, explains how platforms like Dataloop play a key role in building AI pipelines capable of managing real-world use cases with different data types. Thank you to Michael for joining, it was a blast! Learn more about getting started with Dataloop Pipelines here: https://lnkd.in/dhwBGnbb

Meet the people behind the Pipeline — introducing Roni Azriel Here’s a bit about Roni so you know who to thank for Dataloop’s AI magic: ↳ Domain: AI Engineering ↳ Expertise: Crafting AI applications—think Computer Vision, LLM tools, RAG pipelines—and enhancing our platform with deep learning goodness, all while staying on the cutting edge of AI innovation. ↳ Fun factor(s): Roni is a yoga master, hiking enthusiast, and adventurer-in-chief with her four-legged travel buddy, Alfi 🐕 Let’s give it up for Roni [and Alfi]—making AI look easy, one model at a time! 👏👏

Here’s how fine-tuning CLIP within Dataloop Pipelines enables teams to efficiently work with domain-specific datasets, such as analyzing cloud formations and weather patterns: ↳ Pipelines fine-tune these models to improve image-text pairing, enhance search relevance, and boost annotation quality—all within a no-code workflow. ↳ This process also enhances clustering, organizing images into clear, distinct groups that streamline search and retrieval, making data exploration faster and more precise. Learn how to get started with Pipelines: https://lnkd.in/dUvijx-6

Thanks to Ran Wei Baker and Microsoft for Startups for mentioning the case studies and practical content that we love creating for Dataloop customers and the AI community. It's our mission to make AI development faster, more efficient, and providing the best resources to do so!

Pre-trained models often lack the domain-specific precision needed for real-world applications, such as interpreting cloud formations and weather data. Dataloop Pipelines fine-tune these models by leveraging embeddings and feature vectors to enhance image-text association, search relevance, and annotation accuracy—all within a seamless, no-code workflow. Learn how to get started: https://lnkd.in/dbMEviB8

Back in the day, deploying AI models was no quick process—it often required the better part of a weekend. With Dataloop AI + NVIDIA NIM, that time is reduced to just 15 minutes. Faster deployments mean quicker iterations, enabling teams to tackle real-world challenges without delay, and think about AI workflows differently. Learn how to get started via our co-authored blog with NVIDIA: https://lnkd.in/dVXUg2ZU

Our integration with NVIDIA NIM microservices accelerates data prep for LLMs by structuring and enriching various data types. Here’s how it works: The workflow starts by integrating large datasets stored in any major cloud platform, such as AWS, Google Cloud, Azure, etc. After ingestion, the pipeline structures and transforms the data to make it suitable for LLMs by data format: ↳ Image workflow: When an image enters the pipeline, the NVIDIA NEVA-22B NIM microservice automatically annotates key objects, scenes, and elements specific to each project. ↳ Video workflow: For video files, keyframes are intelligently selected and annotated by NEVA-22B, streamlining processing and enriching the video with actionable insights. ↳ Audio workflow: Audio files are processed for language ID, transcribed, and refined for accuracy and context, then indexed in Dataloop for seamless integration into the text workflow. ↳ Text workflow: The text workflow extracts key entities, generates contextual embeddings, and indexes the data to ensure seamless integration. With intuitive plug-and-play functionality, you can bypass complex setup steps and start using NIM microservices for AI projects immediately. Learn more via our co-authored blog with NVIDIA AI linked in the comments

Watch how Dataloop and Google’s #Gemini models simplify multimodal data prep, from syncing 25 million files to structuring images, audio, video, and text. ↳ OCR extracts text from images, speech-to-text transcribes audio, and object tracking delivers insights from video. By the end, data is transformed into AI-ready formats for seamless model training.

From Prototype to Production: Simplifying data prep with Google’s #Gemini models: Media and content teams face a tough challenge: preparing vast amounts of unstructured data for AI. Automate data prep with a Gemini–powered Dataloop pipeline, making it faster to fine-tune your LLMs and extract relevant insights from diverse datasets: 1. Streamline multimodal data prep 2. Enable faster, more accurate training of LLMs 3. Make sophisticated AI workflows accessible to teams of all sizes Learn how to get started via our co-authored blog with Google linked in the comments ⬇️