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Boom! 💥 DataChain is trending on HN - come, join the discussion 🤗 We have been working really hard to rethink how AI changes data processing space - a lot of cool decisions and tech inside! #datachain #dvc #ai #machinelearning #mlops

🚀 Why JSON Metadata is Your Secret Weapon in Gen AI Development As AI developers, we often focus on model architecture and hyperparameters, but here's a game-changer: proper JSON metadata management for your training files. Here's why it matters: ✅ Structured Organization: Standardize your data labeling and categorization ✅ Smart Training Control: Filter datasets based on quality and attributes ✅ Version Control: Track changes and ensure reproducibility ✅ Performance Boost: Pre-filter datasets efficiently ✅ Quality Assurance: Maintain data integrity and provenance 💡 Pro Tip: Start implementing JSON metadata early in your project. It's much harder to retrofit it later! The following example is a way to select files using JSON metadata with DataChain. Try out Open-source DataChain at the repo in the comments. Who else is using JSON metadata in their Gen AI pipelines? Share your experiences below! 👇 #ArtificialIntelligence #GenerativeAI #DataScience #TechTips

What is DataChain? Dmitry Petrov sat down with Swapnil Bhartiya of TFiR to discuss our latest project and how Dmitry sees the data management landscape changing with companies’ greater leverage of their unstructured data and GenAI product development. DataChain addresses the challenges of data curation and preparing high-quality datasets for AI with: 🔹 Improved Functionality: Functions as a data processing framework similar to a data frame but specialized for AI workloads. 🔹 Integration with your storage: Compatible with file storage on S3, Google Cloud, and Azure, enabling easy access to files for data pre-processing 🔹 Data Pre-processing and Curation lineage: Allows data inputs and outputs to be connected in a streamlined, versioned chain. 🔹 Scalability: Operates out of memory, enabling it to handle large-scale data volumes, such as millions of documents, videos, or images. 🔹 Specialization: Optimized for unstructured data processing, especially suited for AI-related tasks. See the link in comments for the full interview and the link to the DataChain open-source repo! Give it a try and let us know what you think! #GenAI #unstructureddata #AI #datamanagement

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📰 Hot off the press! Lots of new blog and video content on DataChain and amazing DVC content from the community plus what we are looking at in the AI/ML ecosystem. Happy October! #GenAI #mlops #Bigdata

𝐄𝐧𝐝 𝐭𝐨 𝐄𝐧𝐝 𝐌𝐋 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐮𝐬𝐢𝐧𝐠 𝐃𝐕𝐂 & 𝐀𝐖𝐒 𝐒𝟑 In this video, you will learn how to build an end-to-end machine learning pipeline. The pipeline will include all the components starting from data injection to model evaluation. You will also use DVC to reproduce this machine learning pipeline and for experiment tracking. Further, you will work on the AWS cloud computing platform and will use services like IM User and S3 bucket for data versioning. Here is a quick recap of what you will learn in this video: 🔹Construct a complete ML pipeline from data injection to model evaluation 🔹Leverage DVC for reproducibility and experiment tracking. 🔹Harness AWS cloud services (IAM, S3) for efficient data versioning Link: https://lnkd.in/dCYpEWDa Follow DVC.ai

🚀 Excited to have presented my research on Data Version Control (DVC) at the Experts Meetup of the Cloud Centre of Excellence, Vodafone Germany (Düsseldorf campus)! 🚀 My paper covered versioning strategies for Data Science & ML projects, emphasizing reproducible and collaborative research methods. The session included live demos and hands-on exercises to showcase key concepts and best practices. Big thanks to the organizers Thomas Pippig, Sven Schuster, Tobias Rittich, Fabian Heck, Jhealyn Samson & Christian Albers for an engaging program with insightful presentations. Appreciation to the DVC team Jenifer De Figueiredo, Dmitry Petrov & Ivan Shcheklein for their fantastic tool improving data and ML model management. Special thanks to Elle O'Brien for her invaluable DVC tutorials! Interested in learning more? Check out my GitHub repo with the live demo code and a comprehensive DVC tutorial: https://lnkd.in/eYHqc-BJ Explore my blog for research papers, open-source code, and AI/ML projects - https://lnkd.in/eTgvFuTD #VersionControl #Git #DataVersionControl #AI #MachineLearning #MLOps Image - © Vodafone

This article by Alph2phi discusses the challenges of managing data and models in machine learning projects and how DVC can help address these issues. The article highlights several key challenges in managing machine learning projects from reproducibility, collaboration, and scalability. The article explains how DVC can help overcome these challenges: 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠: DVC allows versioning datasets and models, similar to how Git manages code. This ensures reproducibility and facilitates collaboration. 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬: DVC pipelines define dependencies between stages of a machine learning workflow, making it easier to reproduce and share the full process. 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐭𝐫𝐚𝐜𝐤𝐢𝐧𝐠: DVC tracks metrics for each experiment, enabling comparison of different runs and model performance over time. Link: https://lnkd.in/dJWdZUx8 The article highlights the key benefits of using DVC as being: 🔹Improved reproducibility of experiments and results 🔹Enhanced collaboration among team members 🔹Scalability for handling large datasets and models 🔹Better organization and transparency of machine learning projects.

Are we on the cusp of a quiet (or not so quiet) revolution in enterprise data processing? 🚀 Daniel Kharitonov just published a deep dive into the emerging "post-modern" data stack and how it's reshaping the way we handle data. It covers: 🔹 The limitations of the current "modern" data stack 🔹 How AI and foundational models are driving change 🔹 What the future of data processing might look like Key takeaways: ➡️ Direct interaction with unstructured data in cloud storage ➡️ Shift from SQL to Python for data manipulation ➡️ Foundational AI models replacing traditional ML approaches Is your organization ready for this paradigm shift? Read the full article to stay ahead of the curve. Link in comments! #AI #techtrends #futureoftech #datascience

Aditya Wardianto outlines a step-by-step workshop process focused on DVC. His article emphasizes the importance of versioning datasets and models to ensure reproducibility and collaboration in data science workflows. 𝗞𝗲𝘆 𝗮𝘀𝗽𝗲𝗰𝘁𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝗗𝗩𝗖: The workshop introduces DVC, explaining its functionality in tracking data and model changes, akin to Git for code. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Participants engage in hands-on exercises, learning how to implement DVC in their projects, including key commands for data management. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗗𝗩𝗖: The article emphasizes the benefits of using DVC, such as better project organization, improved team collaboration, and a comprehensive history of changes. Overall, the workshop aims to empower readers with the knowledge and skills to utilize DVC in their machine learning workflows effectively. Link: https://lnkd.in/dEEv6nRD Follow DVC.ai