SingleStore

🔔 Heads up! We're heading to Amazon Web Services (AWS) re:Invent 2024! Join us to explore the power of SingleStore and Amazon Bedrock, and discover how to use these platforms to enhance your data management and make more informed decisions at scale. 👋 Our team is eager to connect, share insights, and discuss how we can elevate your data management journey at booth 1179. Don’t miss out—come chat with us and let’s innovate together! 🎟️ Register today: https://msql.co/3YAGM7h

Building a Vector Database in Excel with Prof. Tom Yeh This webinar is designed to enhance your understanding of vector data and its significance in today’s data-driven landscape. Don’t miss this opportunity to elevate your skills and explore innovative solutions for managing complex data!

🏎️💨 When you think motorsports, you picture speed & skill—but behind the scenes, it’s all about the data. The Gaucho Racing Formula SAE Team at UC Santa Barbara is taking racing to the next level with a groundbreaking data acquisition system, powered by #SingleStore 🏁🔥 Check out how they built real-time analytics for Formula SAE Racing with SingleStore 🔗 https://msql.co/3ARnzGE via YouTube.

Attention! We're coming to the ETCIO AI Vision event in Bengaluru, India on December 5th! 🇮🇳 Join us at booth 5 to discover the latest in AI innovations and solutions. But that’s not all – we’ll be hosting an exclusive roundtable discussion 🧠 where industry leaders and experts will dive deep into cutting-edge AI trends, challenges, and opportunities. Register today 👉 https://msql.co/491KtHX Curious about what’s coming? Keep an eye out for sneak peeks as we gear up to reveal the exciting topics and speakers at the roundtable. This is your chance to connect with the best minds in the industry! 📅 Save the date – we can't wait to see you there and spark meaningful conversations around the future of AI! #ETCIO #ETCIOAIVision #AIVisionConclave #TechInnovation #SingleStore

Ask anyone to name a dynamic duo and certain combinations inevitably come to mind. In the world of generative AI, that’s Amazon Web Services (AWS) and SingleStore. Learn more about our partnership 🤝 with AWS in this blog post https://msql.co/4fCDyrw and we hope to see you at AWS re:Invent 2024 next month in Las Vegas! 👋

Join us for a quick, interactive lightning talk on building a scalable data environment with AI on December 3rd at Amazon Web Services (AWS) re:Invent! 🕵🏻♀️ Explore the power of SingleStore + Amazon Bedrock to elevate your data management and drive smarter, data-driven decisions. Register today 🔗 https://msql.co/3Za6am6 #AWSreInvent #TechConference #SingleStore

⏳ In just a few hours you'll be able to dive into the details of building powerful enterprise-grade AI agents with SingleStore. 📚 Learn how to Integrate with MLOps Pipelines and get actionable insights on how to incorporate MARS and SingleStore into existing MLOps workflows for seamless, automated model management and many more! 🌟 Speaker Spotlight: 🌟 🔎 Madhukar Kumar, SingleStore's CMO 🔎 Demetrios Brinkmann, Chief Happiness Engineer at MLOps Community RSVP now 👉 https://msql.co/3ClKTgk

Let's understand the two main phases of the #RAG pipeline! The first one is preprocessing phase and the second is inference runtime phase. In the preprocessing phase, raw data is collected and transformed into a suitable format for retrieval. This involves several steps: extracting relevant content from various sources, cleaning the data to remove unnecessary characters, and chunking it into smaller segments. Each chunk is then converted into embeddings—numerical representations that capture the semantic meaning of the text—and indexed in a vector database. This indexing is crucial as it allows for efficient querying during the inference phase.The inference runtime phase begins when a user submits a query. The system generates an embedding for the query and searches the vector database for the most relevant chunks of data based on similarity. The retrieved information is combined with the original query and provided to a large language model (LLM), which generates a coherent and contextually relevant response. This two-phase approach enhances the model's ability to produce accurate answers by leveraging external knowledge effectively while maintaining the generative capabilities of LLMs. ----------------------------------------- Here is my beginner's article on RAG: https://lnkd.in/gE8DcXwu ----------------------------------------- Download my comprehensive RAG E-Book: https://lnkd.in/dCKKTGMQ ----------------------------------------- Also, having a robust data platform is important for your RAG application. SingleStore is one such data platform that can be used as a vector database and also for real-time analytics. Try SingleStore database for free: https://lnkd.in/g7iWVnPf

Three approaches to mitigate LLM hallucinations: ✅ Retrieval Augmented Generation (RAG) ✅ Fine-tuning ✅ Prompt engineering RAG is considered the most sophisticated of these approaches, with recent advancements driving improvements. One such innovation is the use of knowledge graphs in building RAG applications. Learn how knowledge graphs can enhance your RAG applications in our latest blog post 👉 https://msql.co/3UWgrjk