We're almost two weeks from our upcoming webinar featuring Fluree CEO Brian Platz, and Semantic Arts, Inc. President, Dave McComb. This is a must-attend event! What can you expect from the webinar? ⬇️ The duo will discuss strategies and tactics for organizations to: • Assess their own organization's data-centric maturity • Build technological and cultural paths to data-centric architectures • Think Big and Start Small through the MMCE framework (Model, Map, Connect, and Expand) • & much more! Mark your calendar for October 17th (11 AM EST) and click below to secure your spot. Don't forget to share this opportunity with your network! We're looking forward to this insightful conversation with two industry experts. https://lnkd.in/gmEWEF6n #webinar #learningopportunity #Industryexperts
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Mark your Calendars for October 17th! Semantic Arts, Inc. (Dave McComb) and Fluree (Brian Platz) are presenting a webinar titled "Data-Centric Strategies to Break the Cycle of Legacy Addiction" Discussion will be centered around practical steps organizations can take to break the cycle of legacy addiction and move to a more data-centric architecture. Learn more about the continuum from Legacy Amplification --> Legacy Avoidance --> Legacy Erosion --> Legacy Replacement. Use the link below for more information and to register.
Data-Centric Strategies to Break the Cycle of Legacy Addiction | Fluree
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Speed can profoundly impact the productivity and outcomes of #datascience efforts. Learn how to create an end-to-end hardware-accelerated #machinelearning pipeline for large datasets in this new workshop. ➡️ Register for the September workshop: https://lnkd.in/g2BugihS
Enhancing Data Science Outcomes with Efficient Workflows | NVIDIA Deep Learning Institute Workshop
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📣 New Blog Alert! 📣 In case you missed it, Alex is back with another #GenAI blog, looking at the top four vector database options for your #LLM projects, based on differing needs and preferences. Read more here: https://hubs.la/Q02btjT70 #DataScience #MachineLearning
The 4 Best Vector Database Options for your LLM Projects — Advancing Analytics
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Speed can profoundly impact the productivity and outcomes of #datascience efforts. Learn how to create an end-to-end hardware-accelerated #machinelearning pipeline for large datasets in this new workshop. ➡️ Register for the September workshop: https://lnkd.in/ejmhZiQ3
Enhancing Data Science Outcomes with Efficient Workflows | NVIDIA Deep Learning Institute Workshop
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Speed can profoundly impact the productivity and outcomes of #datascience efforts. Learn how to create an end-to-end hardware-accelerated #machinelearning pipeline for large datasets in this new workshop. ➡️ Register for the September workshop: https://lnkd.in/gEus2fZH
Enhancing Data Science Outcomes with Efficient Workflows | NVIDIA Deep Learning Institute Workshop
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Speed can profoundly impact the productivity and outcomes of #datascience efforts. Learn how to create an end-to-end hardware-accelerated #machinelearning pipeline for large datasets in this new workshop. ➡️ Register for the September workshop: https://lnkd.in/gcTrE_p2
Enhancing Data Science Outcomes with Efficient Workflows | NVIDIA Deep Learning Institute Workshop
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Day 1 at the Global Conference, a monumental collaboration between European School of Data Science and Technology (ESDST), Rushford Business School, and Confab 360! 🌐✨ Over 120 cutting-edge paper publications submitted, setting the stage for an intellectual feast. 100+ brilliant minds joined the discourse, creating a big bang of ideas. 🚀📚 These are some of the exclusive snippets capturing the energy and insights from this groundbreaking event! Stay tuned for more. #ESDSTGlobalConference #InnovationHub #ResearchRevolution #ConferenceHighlights"
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Exploring t-SNE vs. SNE in Simple Terms When it comes to visualizing complex data, t-SNE and SNE are two popular methods. Let's break down the differences: 🔹 SNE (Stochastic Neighbor Embedding): ▪ Keeps Neighbors Close: SNE focuses on keeping similar data points close together. ▪ Slower for Big Data: Takes more time and power to process large datasets. ▪ Crowding Issue: Can struggle to show data clearly when reduced to 2D or 3D. 🔹 t-SNE (t-Distributed Stochastic Neighbor Embedding): ▪ Better Visuals: Uses a special method to prevent the crowding problem, making clusters more distinct. ▪ Faster with Large Data: More efficient at handling bigger datasets. ▪ Widely Used: Preferred for creating clear and insightful visualizations of data. Why Choose t-SNE? 🔹 Speed: t-SNE is quicker for large datasets. 🔹 Clarity: Produces clearer and more meaningful visuals. 🔹 Ease of Use: Simple to implement and understand. #DataScience #MachineLearning #DataVisualization #tSNE #SNE #DataAnalysis
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Discover how to boost your GenAI development with Neo4j's graphRAG ecosystem!💪 A big thank you to Michael Hunger and Zachary Blumenfeld for their insightful webinar, highlighting the power of Knowledge Graph builder. Master the art of: ▶️ Creating a knowledge graph from disparate data swiftly ▶️ Utilizing free ecosystem tools for effective GraphRAG Still curious? You can watch the complete webinar on demand: https://bit.ly/4d1ugU7 Keep learning, keep growing with #Neo4j #GraphRAG #Llms. Check out this sneak peek 👇
Kickstart GenAI development with Neo4j's graphRAG ecosystem 💪 This was a great webinar hosted by Michael Hunger and Zachary Blumenfeld where they presented the Knowledge Graph builder: Watch it on demand, and can learn about: ▶️ Quickly starting a knowledge graph from unstructured data ▶️ Free ecosystem tools for talking to your knowledge graph and performing GraphRAG. This video is only a snippet :) Full webinar here: https://bit.ly/4d1ugU7 #Neo4j #GraphRAG #Llms
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