Maithili S.的动态

Data leaders → Watching the news about DeepSeek AI and wondering whether your AI-based data analytics project is about to change (??) become easier (??) or even magically work well (??)? While DeepSeek isn’t free from the usual AI concerns (like hallucinations)... the real story is that AI can’t solve the trusted data problem. That’s why you need metadata intelligence and a semantic layer to ensure your data is AI-ready. This means data that’s 'certified' to work with AI. Once you’ve done that, you can sit back and watch DeepSeek and other LLMs battle it out. You’ll know that with AI-certified, AI-ready data, your AI analytics program will deserve the trust placed in it by the business. #datateams #AI #metadata #datatrust #analytics

?? Embracing the Future of Data Analytics with LLMs and Conversational AI! ?? I had an insightful conversation with DataGPT about their approach. By leveraging RAGs and extensive schema mapping, they've developed a robust conversational data analyst assistant. This tool can perform various analyses, including time trends, anomaly detection, outlier identification, funnel analytics, and comparative analysis, all with relevant dashboards. Andrew Ng highlighted a crucial point in his course, "Generative AI for Everyone": while LLMs excel with unstructured data like PDFs and emails, they face challenges with structured data due to the need for schema mapping. They can interpret and generate textual descriptions of structured data and perform basic analysis, but for tasks like predictive modeling, classification, or regression, they require additional preprocessing. This is where Retrieval-Augmented Generation (RAG) and multi-step chains come into play. ?? A referenced paper shows that 60% of LLM applications utilize some form of RAG, and 30% employ multi-step chains. This innovation is creating efficiencies in the self-service BI space, enhancing productivity. ? At the recent Data and AI Summit, Databricks introduced the AI-BI assistant “Genie.” This powerful tool offers conversational data analysis, utilizing compound AI systems and the metadata of Unity Catalog for seamless conversational analytics. However, to truly harness the power of these advanced tools, it's essential to have the right foundations in place: ?? Data literacy among business users ?? High data quality ?? Clear data lineage ?? Robust metadata maintenance ?? Strong data governance ?? Reliable data pipelines Get ready to unlock new levels of efficiency and insight with these cutting-edge tools! ?? Learn more about these innovations: - DataGPT: [datagpt.com](https://datagpt.com/) - Databricks Genie: [https://lnkd.in/eyhhbbkf) - Compound AI Systems: [https://lnkd.in/eKM3w_3D) - Generative AI for Everyone: [https://lnkd.in/exk7Qfeu) #DataAnalytics #AI #LLM #DataScience #BusinessIntelligence #ConversationalAI #DataGPT #Databricks #Genie #Innovation #Productivity #DataProducts #DataEngineering #Dataquality

How can AI deliver smarter, more accurate responses in a world of complex queries? The answer lies in Retrieval-Augmented Generation (RAG) architectures—an exciting framework that combines retrieval and generation to power next-level AI systems. But what are the different types of RAG, and how do they transform AI workflows? 1. Naive RAG ??Retrieves documents, splits them into chunks, and directly processes these chunks for response generation. ??A simple but foundational approach. 2. Retrieve-and-Rerank RAG ??Refines retrieval by re-ranking the context to prioritize the most relevant chunks. ??Ensures better accuracy and relevance. 3. Multimodal RAG ??Integrates text, images, and videos into the process, enabling responses that span multiple data types. ??Ideal for multimedia-rich use cases. 4. Graph RAG ??Uses graph databases to connect relationships between data, offering richer and more interconnected context. 5. Hybrid RAG ??Combines multiple retrieval methods like vector search and graph traversal for deeper insights and broader context. 6. Agentic RAG (Router) ??Directs queries to the most appropriate models or datasets, optimizing resource utilization and response efficiency. 7. Agentic RAG (Multi-Agent) ??Leverages multiple tools (e.g., Slack, Gmail, web search) to aggregate context from diverse sources. ??A game-changer for handling complex, multi-source queries. Why is RAG crucial for 2025? RAG architectures are redefining AI’s capabilities, enabling smarter, faster, and more adaptable systems. With applications in everything from enterprise workflows to customer support, RAG: ??Boosts accuracy by retrieving the most relevant context. ??Supports multi-source data integration, making AI more versatile. ??Enhances productivity by optimizing task-specific performance. ??Whether you’re in data science, AI development, or enterprise tech, understanding RAG is essential to staying ahead. Which RAG architecture are you most excited to explore? Let’s discuss in the comments! #data #ai #rag #theravitshow

Traditional BI vs AI-Powered Insights: The future of data analytics is here! While Traditional BI tools focus on historical data, AI-powered solutions go beyond—offering predictive insights, automation, and smarter decision-making. At iAlchemy, we empower businesses to transform their data into actionable intelligence, driving innovation and efficiency. Are you ready to make the shift? Let’s connect! https://lnkd.in/gHU4vTT9 #ialchemyinc #infoalchemy #chatsight #clearsight #tracksight #foresight #conversationalai #ai #dataintegration #datawarehousing #datalake #datacleansing #dataquality #datavirtualization #businessintelligence #digitaltransformation #bigdata #analytics #advancedanalytics #machinelearning #ai #masterdata #selfserviceBI #staffaugmentation

?? ?????????? ???????? ?????????????????????? ???? ?????????????? ???????? ???????? ???????????????????? As a Tech Advisor, I'm often asked a question about how I start with RAI. My favourite answer? ?????????? ???????? ???? ?????????????????? ???? ???????? ???????? ?????? ?????????? ??????????????????. But here's the million-dollar follow-up: What format should we use for this inventory? ?? After extensive research into popular industry standards (e.g. ISO 42x) and academic papers, I've found that Google DeepMind's Data Cards playbook stands out as a robust starting point. Developed by the Google's team, it provides a structured approach to documenting datasets. I recommend adopting this framework for your organization. ?? ????????’?? ?????? : 1?? ???????????????????? ??????????????????: The Data Card playbook captures essential facts about ML datasets in 15 themes, making it easier to document and understand complex data. 2?? ??????????-????????????????: Google’s Playbook prioritizes human-centricity, ensuring that stakeholders across the dataset's lifecycle can make informed decisions. 3?? ???? ??????????????????: Playbook categorizes 31 questions into 3 logical groups: Telescopes, Periscopes, and Microscopes, providing a comprehensive overview of the dataset, much better than traditional data cards. ???????????????????????: Offer a wide overview of the dataset, generating enumerations or tags for knowledge management and indexing. ???????????????????????: Provide technical details, adding nuance to telescopes and enabling quick assessments of suitability and relevance. ?????????????????????????: Offer fine-grained details, revealing unobservable human processes, decisions, and assumptions that shape the dataset. ?? Don't wait for regulators to come up with forms.?? Take the first step towards responsible AI compliance by adopting ?????? ???????? ?????????? ????????????????. Ask your data teams to review the 31 questions and identify what they can and cannot do. ???????????????????? ???????? ??????????: ?? Review Google's Playbook and 31 questions. ?? Ask your data cataloging product provider which values are currently captured. ?? Document your data artifacts using the Data Cards framework. Start your responsible AI journey today ?? by comprehensively and transparently documenting in AI datasets. Special thanks to Mahima Pushkarna for making data complexity easier to understand through Data Cards Playbook. Your work is paving the way for responsible and transparent AI. #artificialintelligence #ai #data

???????? ???? ??????????-???????? ?????? ???????? ?????? ???????????????? ?????????????????????? ???? ???????? ?????????????????? ??????'???? ???????? ??????! ?? In today’s AI-driven world, robust data pipelines aren't just a necessity — they're the FUEL that powers everything. ? Without them, AI is just a fancy idea with no real impact. Data pipelines are the backbone of modern data-driven businesses. And they automate the process of collecting, organizing, and transforming data. ...?????? ???????????????? ???? ?????????????????? ???????? ???????????????? ???? ?????????? ????????! Here are the key stages to consider: 1?? ???????? ??????????????: Collect raw data from various sources. 2?? ???????? ??????????????: Ingesting the right and trusted data. 3?? ???????? ????????: Store the raw data in a highly accessible format. 4?? ??????????????????????/??????????????????????: Process and transform the data. 5?? ???????? ??????????????????: Store the processed data for specific purposes. 6?? ???????? ??????????????: Make the data available for analysis and decision-making. Nailing these stages is the formula for creating a data pipeline that goes beyond basic functionality and fully supports your business goals! #data #ai #datastrategy #datascience #aianddata Image from: Semantix

Simplest Explanation of Data Pipelines.

A model is nothing without data, a model is nothing without quality data. In real world, data normally lacks quality but we can improve and enhance our databases with feature engineering. Always perceive in first place good data quality and then yes, you can make your wizardry and deliver your AI product ?? #data #ai #dataquality

?? Unleash the Full Power of Your RAG System with These 5 Secret Hacks! ??? Ready to take your Retrieval-Augmented Generation (RAG) system to the next level? Whether you're working on cutting-edge AI models or simply looking to fine-tune your data retrieval process, these five expert tips will supercharge your system's performance. Let's dive in! 1. Streamline and Structure Your Data ?? Clean Data, Better Results! The foundation of a powerful RAG system is well-organized, clean data. Ensure your knowledge base is logically structured and easy to navigate. Try leveraging a large language model (LLM) to create summaries of your documents and use these summaries to perform targeted searches. This technique will help you pinpoint the most relevant data quickly and efficiently. 2. Diversify Your Indexing Strategies ??? Don’t Rely on Just One Method! While embedding-based similarity searches are great for context, don't forget about keyword-based searches for precision. Combining multiple indexing strategies allows you to handle specific queries more effectively, enhancing the accuracy and speed of your retrieval process. 3. Optimize Data Chunking ?? Find Your Perfect Chunk Size! The size of data chunks fed into your LLM can make or break your system's coherence and context understanding. Experiment with different chunk sizes to strike the perfect balance. Smaller chunks boost coherence, while larger ones capture more context. Fine-tuning this balance will significantly improve the relevance and quality of your outputs. 4. Implement Metadata for Filtering ?? Get the Most Relevant Results! Use metadata to add an extra layer of filtering in your retrieval process. Whether it's filtering by recency or other criteria, metadata ensures that your system retrieves the most relevant data first. This approach is particularly useful in chat or real-time applications where the latest information often holds the most value. 5. Utilize Query Routing and Reranking ?? Specialize and Prioritize for Precision! Enhance your retrieval accuracy by routing queries to specialized indexes and using reranking techniques. Imagine having a team of experts for different query types—whether you need detailed summaries or up-to-date information, query routing ensures your system consults the right “expert” every time. Want to dig deeper? Read the full blog post here: https://lnkd.in/ePbKa5Gp #AI #MachineLearning #DataScience #TechInnovation #ArtificialIntelligence #RAGSystems

How can AI deliver smarter, more accurate responses in a world of complex queries? The answer lies in Retrieval-Augmented Generation (RAG) architectures—an exciting framework that combines retrieval and generation to power next-level AI systems. But what are the different types of RAG, and how do they transform AI workflows? 1. Naive RAG ??Retrieves documents, splits them into chunks, and directly processes these chunks for response generation. ??A simple but foundational approach. 2. Retrieve-and-Rerank RAG ??Refines retrieval by re-ranking the context to prioritize the most relevant chunks. ??Ensures better accuracy and relevance. 3. Multimodal RAG ??Integrates text, images, and videos into the process, enabling responses that span multiple data types. ??Ideal for multimedia-rich use cases. 4. Graph RAG ??Uses graph databases to connect relationships between data, offering richer and more interconnected context. 5. Hybrid RAG ??Combines multiple retrieval methods like vector search and graph traversal for deeper insights and broader context. 6. Agentic RAG (Router) ??Directs queries to the most appropriate models or datasets, optimizing resource utilization and response efficiency. 7. Agentic RAG (Multi-Agent) ??Leverages multiple tools (e.g., Slack, Gmail, web search) to aggregate context from diverse sources. ??A game-changer for handling complex, multi-source queries. Why is RAG crucial for 2025? RAG architectures are redefining AI’s capabilities, enabling smarter, faster, and more adaptable systems. With applications in everything from enterprise workflows to customer support, RAG: ??Boosts accuracy by retrieving the most relevant context. ??Supports multi-source data integration, making AI more versatile. ??Enhances productivity by optimizing task-specific performance. ??Whether you’re in data science, AI development, or enterprise tech, understanding RAG is essential to staying ahead. Join our Newsletter with 137000+ followers — https://lnkd.in/dbZPj6Tu Which RAG architecture are you most excited to explore? Let’s discuss in the comments! #data #ai #rag #theravitshow