How Unstructured and LLMs are simplifying AI development

Bespoke data systems are converging on one truth: everything is becoming a database. Modern DBs already solved the hard parts: efficient scans, filters, joins, and aggs across distributed hardware. The fastest path for AI (including LLM training) is to map every workload onto the “assembly language” of relational algebra. Do that, and you inherit decades of work in memory management, query optimization, and parallel execution. Read more: https://lnkd.in/gkndAW7A #AI #DataEngineering #RAG #LLM #MLOps #Databases

Redis has acquired Featureform, a data orchestration framework that streamlines the delivery of structured data signals for AI applications. This acquisition addresses a key challenge in deploying production-grade AI: ensuring the right data reaches the right model at the right time. For more on how Redis is positioning itself as the "context engine" for AI, enhancing model deployment in enterprises. Read here: https://lnkd.in/eWrm-785 #AI #DataInfrastructure #DataEngineering #AgenticAI 

🚀 New Blog Post We’re excited to share a new editorial article by our CTO, Jerry Shao, PMC member of Apache Gravitino. 📖 Catalogs as Context: How Metadata is Powering the Next Wave of AI As AI systems become more powerful, the real challenge isn’t managing more data — it’s understanding it. In this piece, Jerry explores: 🔹 Why traditional “3Vs” frameworks are falling short 🔹 How metadata is unlocking smarter, safer, more contextual AI 🔹 The rise of agentic data systems 🔹 And how open-source projects play a key role https://lnkd.in/gBQfiVqr

Excited to share my latest project! I recently published an article on Medium about my Proof of Concept (POC) — an Intelligent Chatbot that integrates Retrieval-Augmented Generation (RAG), Text-to-SQL, and AWS Bedrock to enable conversational access to enterprise data. Imagine this: Instead of writing SQL queries or searching through endless documents, you simply ask a question in plain English — and get an instant, data-driven answer. This POC demonstrates how Generative AI can bridge structured and unstructured data, making information retrieval faster, smarter, and more natural. Draft System design and code is available on my github. Read the full article on Medium: #AI #GenAI #Chatbot #RAG #TextToSQL #AWSBedrock #MachineLearning #Innovation #DataEngineering

What if your storage didn't just store your data, but actually understood it? For too long, the most valuable data for AI—unstructured images, videos, documents—has been "dark data", a massive untapped resource that's incredibly difficult to analyze and prepare for ML models. The manual preprocessing pipelines are complex, slow, and simply don't scale. We believe it's time for a paradigm shift. It’s time for smart storage. That’s why we’re incredibly excited to announce the dawn of this new era with two new features in Google Cloud Storage: 🧠 Auto annotate: Automatically enriches your objects with rich metadata using Google's powerful pretrained AI models, so you don’t need to build and manage complex and expensive data annotation and curation pipelines. 🏷️ Object contexts: Allows you to attach custom, actionable, key-value tags directly to your data, making it natively searchable, organizable, and ready for any workflow. This is a game-changer for anyone working with data at scale. With smart storage, you can now: 🔎 Accelerate Data Discovery: Instantly find that "needle-in-a-haystack" image or file across petabytes of data. 🤖 Streamline Data Curation for AI: Automatically identify attributes to build more balanced and effective training datasets, reducing model bias. 🛡️ Scale Data Governance: Proactively identify sensitive data (PII) and trigger automated governance actions to manage risk and compliance. ➡️ Read the detailed blog post by myself and my colleagues Asad Khan / Manjul Sahay to get all the technical details: https://google.smh.re/5CPI Let's build the future of data, together. #GoogleCloud #SmartStorage #AI #MachineLearning #DataManagement #CloudStorage #DarkData #GenAI #DataGovernance

This article explores how Google's Auto Annotate and object contexts can transform unstructured data into valuable AI datasets. I found it interesting that these tools not only streamline data discovery but also enhance the management of vast information resources. What strategies are you implementing to harness unstructured data in your organization?

𝐀𝐈 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐚 𝐦𝐢𝐫𝐚𝐠𝐞. Large Language Models are powerful, but the data that truly matters lives in relational databases. With 𝐌𝐚𝐫𝐢𝐚𝐃𝐁 𝟏𝟏.𝟖 𝐆𝐀 𝐋𝐓𝐒, vectors are now a 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 in a familiar, easy-to-manage stack – making it natural for AI to connect directly to your data. Our vision is clear: to make it as easy for AI to access relational data as it is to search the Internet. And our Board fully shares this vision. 👉 Read the full blog post here: https://lnkd.in/dt2SHS2q

Dark Data or the large amounts of unstructured data in your organization holds rich insights—but it's impossible to search or use at scale. We're announcing two features that make your data intelligent:  Auto annotate: Automatically generates rich, AI-generated metadata about your data Object contexts: Lets you attach custom, instantly searchable tags directly to objects Read our smart storage vision by Asad Khan and Manjul Sahay: From dark data to bright insights: The dawn of smart storage [https://lnkd.in/e5UaS_CE] #GoogleCloud #SmartStorage #AI #MachineLearning #DataManagement #CloudStorage #DarkData #GenAI Asad Khan Manjul Sahay Sameet Agarwal

#Couchbase ups database vector search, indexing capabilities with a three-pronged approach to storing and searching vectorized data to make building AI tools trained on massive amounts of relevant info faster - via Eric Avidon from TechTarget, Devin Pratt from IDC, and Matt Aslett from ISG. https://lnkd.in/gaxkCtmy

🌈☁️ Welcome to the NoSQL Universe! Meet BiddyBot, your tiny data explorer 🤖✨ While traditional (relational) databases store info in rigid tables, the NoSQL Universe is a flexible cloud of endless possibilities — perfect for the wild, unstructured data that powers modern AI and machine learning. 💾🧠 💡Here’s what makes NoSQL magical: • Flexible Data Shapes: It can store text, images, audio — anything your model needs. • High Scalability: Handles billions of entries without breaking a sweat. • Fast Access: Fetches data instantly for real-time ML pipelines. • ML-Ready: Embeddings, vectors, and documents flow directly into your AI models. In short Relational = neatly stacked drawers. Non-Relational = a living, breathing cloud of knowledge. ☁️💫 #AI #MachineLearning #DataEngineering #NoSQL #Databases #CuteTech #BiddyBot #DataScience