Cassie Kozyrkov

The Data Analytics Evangelist here checking in. Data Analytics can be found in every industry today. Even Hollywood is embracing data analytics. See the article below: "Big Data and Hollywood, a Love Story. https://lnkd.in/ethSCEV9

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Heading to Databricks's #DataAISummit? Connect with fellow attendees interested in improving LLM evaluations and feedback techniques by grabbing the last available spot for Arjun Bansal's braindate at the event ⬇️ https://lnkd.in/gAT6PTYt If you didn't get a spot in the group, you can still learn about Log10's approach in Arjun's conference session, AutoFeedback: Scaling Human Feedback with Custom Evaluation Models, or at Log10.io #LLMOps #LLMs

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Prosper Insights & Analytics Uncovers a Shift in National Sentiment on the Vitality of the American Dream The survey reveals a lukewarm belief in the American Dream across all age groups that hard work and sacrifice can lead to home ownership and upward mobility. https://lnkd.in/gb3c-43d #Americandream #economicexpectations #upwardmobility #Prosperinsights&analytics #nationalsentiment #homeownership

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AtScale + Snowflake + Delphi Labs - Unlock the Potential of Self-Service Analytics – https://bit.ly/3UOjIBu The Future of Self-Service Analytics with Snowflake, Semantic Layers & LLMs - How to Deliver Self-Service Analytics on Snowflake Using a Semantic Layer and LLMs Join AtScale’s webinar on May 22nd to unlock next-level data analytics with Snowflake, AtScale, and Delphi Labs. Learn from industry experts how to integrate Snowflake's powerful platform with AtScale's Semantic Layer and leverage LLM’s for transformative insights. Register today! Wednesday, May 22, 2024 2:00 PM ET (11:00 AM PT) | 60 mins Featured Speakers • Dave Mariani, Founder & CTO, AtScale • Alex Ross, Senior Solutions Engineer, Snowflake • David Jayatillake, CEO, Delphi Labs • Michael Irvine, Co-Founder, Delphi Labs You’re invited! - https://bit.ly/3UOjIBu Learn how to integrate Snowflake's data platform with AtScale’s Semantic Layer and Large Language Models (LLMs) to enhance self-service analytics capabilities. You'll walk away with actionable insights to harness the full potential of this innovative approach. Don't miss this opportunity to enhance your data strategy and stay ahead in today's dynamic data landscape. In this webinar, we’ll cover: • The Foundation of Data Management: Learn from Alex Ross, Senior Solutions Engineer at Snowflake, about the fundamentals of an efficient data platform. • Introduction to Semantic Layers: Discover how a semantic layer can simplify data accessibility and enhance business insights with Dave Mariani, CTO at AtScale. • Power of LLMs in Analytics: Delphi Labs will present a live demonstration of how LLMs integrated with a semantic layer can revolutionize data interaction. Who should watch This webinar is designed for those eager to streamline their analytics processes and make data-driven decisions faster and more efficiently.

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Want to learn more about how to get started using Snowflake Cortex LLM Functions? Check out this post from Jon Coulter detailing how to use these tools to quickly classify data, empowering Data Engineers with Data Science tools! https://lnkd.in/gMTFT5xQ

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Wake up! LLMs have jolted companies from their data slumber. But mountains of disjointed info, scattered silos, ignored guidelines, and skyrocketing costs are getting in the way. Sanjay Deshmukh tells us how Snowflake is stepping up as the new data sheriff and bring order to this new chaotic data frontier. #data #snowflake #LLM #generativeai #AI #datamanagement

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Welcome to the first of our three-part SPARC (Short Podcasts by ARC) series with SAS, where we'll be unpacking the complexities of adopting artificial intelligence (AI) in the industrial sector. As an industry analyst at ARC Advisory Group, I've had the opportunity to speak with numerous organizations, and one thing is clear: while the potential of AI is immense, so are the challenges. In this series, I'll be exploring industrial AI challenges with Bryan Saunders, Global Director and Head of IoT Industry Consulting at SAS to share some key insights. In part 1, we're diving deep into the foundational issue: data management. Watch or listen to Part 1. #IndustrialAI #GenAI #IndustrialAnalytics #IndustrialDataFabric https://lnkd.in/gU2tyaGX

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Prosper Insights & Analytics Uncovers a Shift in National Sentiment on the Vitality of the American Dream Is the American Dream Fading? In a groundbreaking survey conducted in May 2024, Prosper Insights & Analytics has tapped into the pulse of 8,580 U.S. consumers, unveiling a significant shift in attitudes towards the American Dream. The results paint a picture of a nation grappling with doubt and uncertainty, as less than half of both younger and older Americans believe that hard work and sacrifice can still yield the rewards traditionally associated with the American Dream. https://lnkd.in/gb3c-43d #AmericanDream #sentiment #consumers #Attitudes #uncertainty

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With one line of code, users can process requests on Snowflake's Cortex #AI in a different region even if an LLM isn’t available in theirs. Snowflake #ArtificialIntelligence #GenerativeAI

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Data leaders need to ask vendors hard questions when evaluating data pipeline tools that feed GenAI. Here are 3 big ones. And join my BARC webinar on Tuesday with Luke Roquet, COO of Datavolo, to explore all 20 questions. This matters because GenAI language models fail without well-prepared data inputs. https://lnkd.in/gv4mVKCA > Does this tool enable users to manage the full lifecycle of a GenAI data pipeline? Your tool should enable your team to design, test, deploy, observe, and orchestrate GenAI data pipelines.   - Design. Select and configure pipeline elements such as sources, targets, and transformation tasks. - Test. Test the pipelines, perhaps by comparing the performance of different versions in a development environment, or by checking whether vectors align with the embedding model. - Deploy. Release the pipeline to production. Iterate by branching a pipeline version out of production, revising it, then deploying the new version to production. - Observe. Monitor performance and tune pipelines to optimize pipeline performance and output quality. - Orchestrate. Schedule, execute, and synchronize tasks across pipelines as well as the applications that consume their outputs. > What types of transformations does it perform? Transforming diverse, unstructured datasets into usable embeddings can require sophisticated transformation methods. Ask your vendor about their support for capabilities such as the following. - Normalize source content to create consistent formats and data ranges. - Adjust chunk size to determine the optimal size for supporting accurate similarity searches. You might want to test one size for a subset of data, review results, then adjust. - Support multiple chunking techniques, such as fixed-size chunking or content-aware methods that split sentences, create hierarchical structures, and so on. - Enrich chunks by appending metadata that describes objects’ origin, usage, or content. This helps create and update embeddings in the vector database. > What types of AI implementations does it support? Your pipeline tool should prepare and deliver data to support the following scenarios. - RAG. As described earlier, GenAI application uses a similarity search to find and retrieve embeddings from a vector database, then adds them to user prompts. - Fine-tuning. Also as described earlier, data science teams retrieve and compare embeddings from the vector database as they fine tune the LM. They also might use content from an object store. - Multi-faceted AI workflows. The GenAI application and other functions (natural language processing, predictive ML, etc.) retrieve content from the vector database or object store. - Naive RAG. The GenAI application searches through key words to find relevant files within an object store. It retrieves them and augments the user prompt with them. Sam Lachterman Shawn Rogers Florian Bigelmaier #genai #datapipeline #ai

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An interesting study from Bill Schmarzo and Mark Stouse in Data Science Central "We have been vocal about my belief that organizations focusing on GenAI-driven productivity improvements rather than GenAI-driven innovation improvements are missing the bigger prize. GenAI tools can create value for an organization, but that increase in value is inconsistent. On one hand, GenAI tools can improve the productivity (efficiency) of below-average workers. On the other hand, they can significantly boost the innovation (effectiveness) of top-performing workers who better understand critical value-creation concepts such as data science, design thinking, and economics." #ai #genai #automation #productivity #innovation #teckedin https://lnkd.in/g7kgMnxf

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The 6 daily non-negotiables that give me completely clarity in my life that Mark Gandy and I spoke about on his podcast: 1. German lesson 2. 45 minutes of exercise 3. Read 10 pages of non-fiction 4. Review short- and long-term goals 5. Write 3 posts on LinkedIn of variety of topics 6. Reach out to 5 people I don't know to build my network I started this habit about 300 days ago. Learn more about why in this interview.

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"This article explores the evolution of search strategies, from fundamental index seeks to modern vector searches powered by machine learning." Top 3 Strategies to Search Your Data by Shawn Shi

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✨ Master AI in Snowflake: Enhance Your DBT Workflow ✨ Our Senior Data Engineer, Jaime Andres Salas, guides you in integrating LLMs with Snowflake Cortex into your DBT transformations. Key points: 🔹 Sentiment analysis with AI 🔹 Join sentiment data with reviews 🔹 Create recommendations for feedback https://hubs.ly/Q02BX1sW0 👉 Read the full article #AI #Snowflake #DBT #DataScience #LLM

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The Art of Data Buck-Passing 101: Mastering the Blame Game in Data and Analytic Teams. In this humorous blog, learn the art of blaming someone else when things go sideways. https://hubs.ly/Q031Z_gR0 #dataops #humor #datajourney #dataobservability

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The new #Planner has the ability to #Baseline! I'm jumping up and down seeing this new feature that PMs have been asking for! 👏🥂🎉 Reminder - Learn more about the new Planner with me April 30 at the Generative AI Day for PMs conference - https://lnkd.in/eMsC_ART

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A good article in Data Science Central from Bill Schmarzo "Data-driven economics focuses on understanding and maximizing the economic value of data. Unlike traditional assets that depreciate, data appreciates as it is used across different scenarios, making it a powerful driver for business transformation. This pillar emphasizes the importance of monetization, sustainable ROI, and strategic use of data to create value. By treating data as a reusable asset, organizations can prioritize initiatives that offer the most significant economic impact, ensuring that innovation efforts are financially viable and scalable." #ai #dataanalytics #dataanalysis #datamanagement https://lnkd.in/giaCpxxR

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Check out this InsideAnalysis podcast with our CEO, Justin Borgman! Eric Kavanagh and Justin talked about analytic architecture, the #Icehouse, and how Starburst gives data teams the freedom to be curious. Listen to the whole episode here: https://okt.to/TJ8bMo #analyticarchitecture #bigdataanalytics #apacheiceberg #trinodb

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