Edward Santos’ Post

Tellius’ decision intelligence platform aggregates and processes data from multiple sources to accelerate analysis, speed insights, and improve decision-making. https://bit.ly/40vFkCy #Analytics #DecisionIntelligence #AI #MachineLearning Tellius

There's something new on our blog! Check out how you could easily use your own data with RAG solutions to talk with your data! https://lnkd.in/dST9gZ8c #openai #rag #llm #ai

As we mentioned, unleashing the power of AI isn't just about having data—it's about having the right data. Discover how Large Language Models (LLMs) are revolutionizing the way businesses extract value from both structured and unstructured information. Are you ready to unlock the hidden potential in your data? Read more here in our latest blog post https://bit.ly/3NEo3CN #AIInBusiness #DataIntelligence #genAI #RaguAI

GenAI sorely needs governed, accurate inputs. So we should expect data observability companies to start monitoring and optimizing vectorized text that supports language models. Monte Carlo just announced plans to do this. Thank you Eric Avidon of TechTarget for including my perspective about their announcement. Here is his article, including insights from co-founder Lior Gavish, with excerpts below. https://lnkd.in/gcffQbKq "Data observability specialist Monte Carlo on Wednesday unveiled new features designed to ensure data quality, including integrations with vector databases and Apache Kafka. "Data observability is the process of monitoring data as it progresses through the pipeline from ingestion through analysis to make sure the data used to inform decisions is accurate and up to date... "Vector databases have gained importance in the year since OpenAI released ChatGPT, which marked a significant improvement in generative AI and large language model (LLM) technology... "Eckerson Group research shows under a quarter of data experts rate their data governance and data quality controls sufficient for AI and machine learning initiatives, including generative AI. "Monte Carlo is taking a significant step to help them ensure accuracy by observing the quality of text files as they are transformed into vectors and embedded into vector databases," said analyst Kevin Petrie of Eckerson Group. "They also ensure the quality of database records that will complement generative AI in many of these initiatives." "Vector databases are earlier in the adoption curve with customers," Gavish said. "They're starting to look at them. We're hearing that customers are building pipelines for generative AI and need to add observability into that. But the landscape there is early. It's not like everyone is using vector databases yet." "Beyond vendors such as Pinecone and Chroma that specialize in vector databases, many database and broader data management vendors offer vector databases within their larger offerings. That could be an opportunity for Monte Carlo. "For example, both Neo4j and SingleStore now offer vector search capabilities. "Now that they've taken this first step with vector databases, Monte Carlo should round out its support by integrating with the full range of vector databases, including both dedicated platforms and broader platforms that include vector capabilities," Petrie said. Wayne Eckerson Eckerson Group Jay Piscioneri #dataobservability #generativeai #genai #vectordatabase

Databricks has introduced 𝘎𝘦𝘯𝘪𝘦, a new AI/BI tool that marks a significant advancement in business intelligence. By integrating generative AI with robust data governance, Genie addresses real-world challenges and moves towards true self-service BI. My latest article explores: • 𝐆𝐞𝐧𝐢𝐞’𝐬 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: How it uses a specialized AI system to tackle complex data scenarios and provide accurate, trustworthy answers. • 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐚𝐧𝐝 𝐓𝐢𝐩𝐬: Practical advice on setting up and using Genie, from data preparation to overcoming current limitations. • 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬 𝐚𝐧𝐝 𝐀𝐫𝐞𝐚𝐬 𝐟𝐨𝐫 𝐆𝐫𝐨𝐰𝐭𝐡: The potential benefits for your data workflows and areas where Genie still has room for improvement. Discover how Genie can streamline your data analysis process and empower both data analysts and business users. 🔗https://lnkd.in/dNm6Xwi2 If you want to keep up with tech tips and business insights, follow Closer Consulting on Medium. #Databricks #Genie #GenAI #DataAnalysis #BusinessIntelligence #DataScience

I really like Genie for a simple way to give your internal users who don’t know sql ways to ask questions of your internal data with some great built in tools to lower hallucinations. See this practitioner review.

For many CDOs, building a data-driven culture is the holy grail of all data-related efforts and investments. Why is this? The general thinking is a strong data culture will enhance organizational decision-making. Essentially, you create an operating environment that uses data whenever and wherever possible to enhance business efficiency and effectiveness. What do you think? Learn more here 👉 https://lnkd.in/gTgwS94E #datadriven #digitaltransformation #analytics #ChatGPT #NLP #ecommerce

🤔 𝐇𝐨𝐰 𝐂𝐚𝐧 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐀𝐈'𝐬 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥? The latest innovation from Microsoft, GraphRAG, is set to revolutionize how we use AI for data retrieval and generation. Here’s why it’s a game-changer: 🧠 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆? GraphRAG stands for Graph Retrieval-Augmented Generation. It's an advanced version of traditional RAG systems designed to provide more accurate and context-rich responses from AI models by leveraging knowledge graphs. 🔄 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Traditional RAG: When you ask a question, it performs a semantic search to pull relevant information and feeds it to the language model. GraphRAG: Goes a step further by extracting entities and their relationships from the data, allowing the model to understand and provide more nuanced and accurate answers. 🌐 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬: Data Chunking: Like traditional RAG, GraphRAG divides data into chunks but also extracts useful entities and their relationships. Semantic and Graph Search: Combines semantic search with graph-based context to enhance the relevance and quality of responses. 📈 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Private Datasets: Ideal for businesses with extensive private datasets, allowing for detailed and accurate data retrieval. Enhanced Q&A: Provides high-quality, summarized answers by understanding the relationships between different data points, making it useful for customer support, research, and more. 💡 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: Entity Summarization: Automatically summarizes entities and their relationships. Community Summarization: Uses pre-existing community relationships to add more context and meaning to the data. Topic Detection: Identifies and organizes data around specific topics for better insight. 🛠️ 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆: Setup: Install GraphRAG and initialize your project with simple commands. Integration: Easily integrate with models like GPT-4 or OLama by configuring API settings. Data Indexing: Index your documents to create a detailed knowledge graph. Query Execution: Perform both global and local searches to get detailed or focused answers from your indexed data. Microsoft’s GraphRAG system not only improves the quality of AI responses but also makes it easier to manage and retrieve complex data. This system is poised to take AI data processing and retrieval to the next level. 🔗 How do you see GraphRAG impacting your data retrieval and AI projects? Are you ready to implement this advanced system for better accuracy and insights? #AI #DataRetrieval #GraphRAG #Microsoft #TechnologyInnovation