iterative.ai

Exciting News! 🎉 Design Partnership with Sigen AI. We are thrilled to announce a design partnership with Sigen AI! Through this collaboration, Sigen AI’s selective anonymization technology will now leverage our cutting-edge solutions to enhance security, reliability, and privacy compliance for clients worldwide. Stay tuned as we continue to push the boundaries of privacy-first AI innovations and deliver even more robust and reliable solutions together! #DataChain #PrivacyCompliance #Partnership #Innovation #SigenAI

"... metadata marts could play a key role in making video data more accessible and structured for model training and analysis ...". Read the new #datapains post by Simon Thelin. He is reviewing DataChain and gives some excellent recommendations. We definitely plan to add Iceberg support in open source (Studio is already using ClickHouse which is scalable and will support Iceberg soon). Stay tuned!

Watch an excellent PyData Berlin talk by Julian Wagenschütz (Volkswagen Group) on automating and managing fluid simulations with Python and DVC. See details 👇 and more links in comments. Julian Wagenschütz suggests using DVC, built on Git, to streamline simulation iterations and track results. DVC keeps it lightweight (no need to run servers and such - CLI, Git, basic Python) while making the whole process way more manageable and scalable. The usual DVC building blocks are utilized to achieve this: ✅ DVC data versioning makes sure input data is saved and attached to an iteration and can be restored or access anytime in the future; ✅ Lightweight CLI pipelines declaratively describe and run data processing and DVC captures products of this processing; ✅ Finally, metrics, parameters are captured and also attached to Git and iterations - to compare, visualize result; See the full talk here: https://lnkd.in/ebPJteUc #dvc #simulations #OpenFOAM #dataversioning #reproducibility #simops

DataChain organizes and makes your AI data queryable! What does it mean? Why? 👇 All efficient AI teams have an excellent data hygiene - they utilize databases, ETLs, custom scripts / tools to effectively build a metadata layer on top of their binary data (videos, images, etc). This is essential to make data accessible, understand it, clean it - build better datasets. Some examples and ideas we've seen: 🎥 Photoroom - see the link to the talk by Eliot Andres in the comment 🧊 Iceberg for metadata (and sometimes binaries) + Spark - is one of the default choices (but usually requires data engineering skills / team) 🐘 Smaller scale - DVC + CSV files, or Postgres + some custom ETL to feed it DataChain is an open source library 🔗 (and SaaS platform for collaboration and scale) that implements this idea at scale + our goal was make it easy to use by ML teams. Give a try! See the links in the comments.

Under the hood DataChain combines power of warehouses with distributed clusters with proper data access patterns to process millions of video, images audio files: ☁️ Never copy data. Store references to files instead. (while still preserving versioning, data loading, efficient processing) ⚡ Use warehouses under the hood (e.g. ClickHouse) to store metadata and perform as many operations inside it (e.g. filters). ⚙️ Distributed compute that runs close to the data to compute Python-based UDFs 🤏 Data access. Pre-fetch, batching, caching, streaming - different workloads require different ways of using data. #unstructured #datachain #dvc #machinelearning #opensource

A quick glimpse from our CEO, Dmitry Petrov, into ETL and data governance aspects of the DataChain and our SaaS for unstructured data processing: ✅ Each dataset is immutable, versioned, and has fingerprints for all data objects to reproduce; ✅ All dependencies are tracked and saved: code, datasets, raw data sources; ✅ ETL can be run automatically or on schedule to produce new versions of the datasets; Interested to learn more? Contact us here https://datachain.ai/ Open source version is available here to try: https://lnkd.in/emFvJD84 #unstructured #dvc #datachain #machinelearning

DataChain got hand-picked on `r/Python` as one of the top 2024 tools in the "AI / ML / Data" category 👌. Thanks folks, we are also super convinced that we need better tools for unstructured / AI data management. It is still a very hard problem and existing platforms don't address all the needs. Meanwhile there is a very strong and growing demand from AI companies, from all the companies that now do RAGs and other apps that tap into unstructured data. We are working hard on DataChain and DVC to make the whole data processing for images, audio, texts, pdfs, etc scalable, faster, and pleasant experience. Stay tuned, more to come! Quote: "Our selection criteria remain focused on innovation, active maintenance, and broad impact potential. ...." #datachain #dvc #unstructured #machinelearning #opensource

Dealing with a lot of unstructured or multimodal (audio, pdfs, images, videos) data is hard. We clearly need new tools for unstructured data: processing, governance, analytics, preparing it for RAGs, etc, etc. This small video by Ivan Shcheklein is a glimpse into how our DataChain SaaS helps with those aspects: - stream audio files from tar or wds archives! - enrich, prepare, version, publish datasets ... 🚀 - bonus! 🤗  is now natively integrated as a storage provider! Colab notebook: https://lnkd.in/g4W4qF4i Jupyter Notebook: https://lnkd.in/gTbj8ZG2 DataChain Repo: https://lnkd.in/emFvJD84 #huggingface #machinelearning #unstructured #dvc #datachain

DataChain hit 2000 stars ⭐ on GitHub a week ago. Thank you for your interest and support 🤗 It was built to address those needs and pain points we saw in the DVC community when people have to deal with millions of files (e.g. images, pdfs, audio, etc). ❓How to "query" them to find similar, deduplicate, based on some insights, etc ❓What if those are tar or WebDataset archives ... 🤯 ➡️ How to apply transformations (e.g. LLMs or any other models) at scale to get insights and do analytics on top of that? 🧑🏻🤝🧑🏻 How to collaborate - share datasets with those insights? Version and reproduce those 💰What about ETLs with granular updates (it's expensive to run GPUs to get embeddings) ... And many, many more questions ... We've just scratched the surface and more features to come, but DataChain (open source and enterprise SaaS) is already saving many many data engineering and ML researchers hours. https://lnkd.in/emFvJD84 https://datachain.ai How do you manage your unstructured data? #unstructured #machinelearning #opensource #dataengineering #dvc #datachain

DataChain hit 2000 stars ⭐ on GitHub a week ago. Thank you for your interest and support 🤗 It was built to address those needs and pain points we saw in the DVC community when people have to deal with millions of files (e.g. images, pdfs, audio, etc). ❓How to "query" them to find similar, deduplicate, based on some insights, etc ❓What if those are tar or WebDataset archives ... 🤯 ➡️ How to apply transformations (e.g. LLMs or any other models) at scale to get insights and do analytics on top of that? 🧑🏻🤝🧑🏻 How to collaborate - share datasets with those insights? Version and reproduce those 💰What about ETLs with granular updates (it's expensive to run GPUs to get embeddings) ... And many, many more questions ... We've just scratched the surface and more features to come, but DataChain (open source and enterprise SaaS) is already saving many many data engineering and ML researchers hours. https://lnkd.in/emFvJD84 https://datachain.ai How do you manage your unstructured data? #unstructured #machinelearning #opensource #dataengineering #dvc #datachain