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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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🔥 🚨 Breaking news 🚨 🔥 Meta releases Llama 3.1 absolutely destroying the benchmarks. More info here: https://llama.meta.com/ 𝗧𝗵𝗲 𝗔𝗵𝗮! ✅ Includes Tool Use (Upload a dataset and analyze it. Prompt model to plot graphs and fetch market data) ✅ Multi-Lingual Agents (eg. translate photos into video in Spanish) ✅ Complex reasoning (understand and solve logic problems) ✅ Long Context (Upload, analyze, and summarize large documents with up to 128k tokens) ✅ Coding Assistants (for complex end2end programming workflow) Summary of Llama 3.1 405B benchmarks: 🏆 88.6% on MMLU (vs. 88.7% GPT4o, 88.3% Claude 3.5 Sonnet) 🏆 88.6% on IFEval (vs. 85.6% GPT4o, 88.0% Claude 3.5 Sonnet) 🏆 96.8% on GSM8k Math (vs. 96.1% GPT4o, 96.4% Claude 3.5 Sonnet) Rah-rah.. another slightly better model.. why should you care? 🎉 This is an inflection point where for the first time an open weight model has come this close, and in some areas beat the leading closed source frontier models. This is AI's Linux moment where Open is the new standard. 🎉 Llama3.1 license allows for any developer to distill a smaller model optimized for their use cases, use it legally for generating synthetic data, and as a teacher model to improve smaller models. OpenAI, Anthropic etc. don't allow this. 🎉 A 405 billion parameter model being able to outcompete models 3x it's size makes the performace/cost ratio deliver great business ROI 𝗧𝗵𝗲 𝗨𝗵-𝗢𝗵! 😔 Meta models are open-weight not open-source like IBM granite or Mistral. 😔 Meta does not provide any indemnification for their models. Which means a third party sues the enterprise for copyright infringement related to their use of the Llama Materials, the client is responsible for defending themselves and holding Meta harmless. If the enterprise sues Meta for copyright infringement, the license agreement will terminate. IBM's granite models have set a very high bar for indemnification that needs to be the new standard. https://lnkd.in/gVgYT9Yt 😔 Since Meta released Llama under their custom license, it has a lot of constraints, and doesn't come with patent protection like Apache 2.0 (eg. IBM granite, Mistral). 😔 Meta enforces you to include “Built with Llama” on any app that leverages Llama eg. on chatbot UI, app UI, website, blogpost etc. #meta #watsonx #ibmconsulting #ai

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🔍 Just read a fascinating paper on the future of data ecosystem business models in the emergent "Artificial Intelligence of Thinks" (AIoT) space! Very useful systematic research that yields some key insights on how companies are transforming data into value in this developing ecosystem. https://lnkd.in/ert4nU7X 🔄 FOUR DISTINCT BUSINESS MODEL ARCHETYPES IN THE AGE OF AI ARISING 🤝 Adaptive Partnership - High control + High customization - Focuses on developing close, collaborative relationships with customers - Offers deep insights and personalized solutions - Best suited for strategic, long-term business relationships 👥 User-Centric - Low control + High customization - Prioritizes customer experience and flexibility - Enables broad market reach and scalability - Emphasizes user autonomy in configuring solutions ⚡ Standardized Efficiency - High control + Low customization - Focus on operational excellence and cost reduction - Streamlined processes and standardized offerings - Ideal for high-volume, routine operations [3] 🔄 Flexible Transaction - Low control + Low customization - Transaction-focused with minimal ongoing engagement - Emphasizes automation and self-service - Suitable for commoditized offerings 💎 AIOT BUSINESS MODEL VALUE CAPTURE STRATEGIES: 📊 Responsive Monitoring: Goes beyond traditional monitoring by enabling instant actions based on real-time data, including automatic corrective functions and predictive maintenance to prevent failures 🤖 Object Self-Service: Drives objects toward independent functionality with less human intervention, incorporating self-adjustment, replenishment, and self-operation capabilities 💡 Insights as a Service: A data-driven value proposition that leverages AIoT to collect, analyze and deliver real-time and collective insights that give meaning to massive amounts of data, enabling customers to make informed decisions   ✨ Enhanced User Experience: Focuses on improving customer interaction through digital add-ons and customizable interfaces and content that adapt to individual users' preferences and behaviors over time 💡💡 The research shows successful AIoT implementations balance control and customization to create sustainable competitive advantages. 💡💡 🤔 Where do you see your organization fitting in this framework? #AIoT #DigitalTransformation #Innovation #BusinessStrategy

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Serious question about synthetic participants. If we assume that synthetic participants can fill out a survey good enough to stand in for real participants, why do we need the survey at all? If we have a question, and we're saying that we believe the AI + data (synthetic participants) knows the answer, then why have the AI pretend to be 500 people, take a survey, analyze the survey, and get at the answer through a very labor intensive market research pipeline? Why not just ask the AI what the answer is?

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Unlocking Cause-and-Effect Relationships in your “Real World Data” Xplain Data GmbH | Discover Causality Data's Causal AI algorithms enable companies in all industries to identify the few potentially causal relationships in their real-world data that are hidden behind a multitude of trivial correlations - the basis for evidence-based decisions to optimize business processes. By knowing why something is happening, users can intervene in their business processes in a targeted manner to eliminate the causes of errors or achieve a desired effect. Key services: - Innovating Causal AI technology - Patented, object-centric data storage system - Identifying potential causal relationships in “Real World Data” (Causal Discovery) - Discovering causes for failures or (un)wanted outcomes in complex data - Empowering customers with insights for better decision making and intelligent interventions to impact their KPIs Whether your business focus is on the “customer”, “patient”, or technical device like a “machine” or a produced “part”, holistic understanding is vital for business success. Today’s databases often struggle to provide this holistic view of the business object, dividing objects into fragmented parts across multiple tables. Xplain Data's object-centric data storage provides the necessary 360° view and comprehensive analysis of your object of interest. This holistic perspective is critical for uncovering cause-and-effect relationships in real-world data and is the foundation for successful causal discovery. Learn more about why correlation doesn't imply causation, the difference between predictive models and causal discovery, and why understanding cause and effect is crucial to intelligent business management. Checkout here: http://www.xplain-data.com Follow Aman (AI-Man) and hit the notification 🔔 bell button for more posts related to exciting new AI tools 😃 #CausalAI #DataAnalytics #BusinessIntelligence #CausalDiscovery #RealWorldData #SmartDecisions #Innovation #Technology

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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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SEAL is a new model benchmarking service from Scale AI that fills a big gap: an additional objective, third-party, trustworthy way to compare models. People distrust many benchmarks because they worry that models are optimized to perform well on them—including by training on the underlying data (like cheating on a test by memorizing the questions and answers in advance). The LMSys Chatbot Arena (https://chat.lmsys.org) addresses this by having humans evaluate model output in blinded head-to-head tests. The new SEAL leaderboard (https://lnkd.in/gdCZzRnz) does something similar, but uses "thoroughly vetted experts using domain specific methodologies" to do the head-to-head evaluation. Sounds much more expensive, but they just raised $1 billion, so I think they can afford it.

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Round up time! Four recent articles about data quality and my spicy take 🌶️ Marc Di Gaspero and Adrien Vermeirsch from Potloc have done extensive research on data quality, and now Marc is sharing some key insights! Out of over 26,000 entrants, the average was 22 daily survey attempts (!), with similar results across five major online panels and five sample exchanges/DIY solutions.  The best part: High-frequency survey takers is an easy problem to solve - why don’t we? Because less traffic = less revenue. 🤷 In the article “$urprise! Re$pondent$ are not altruistic,” Vignesh Krishnan at Rep Data discusses the significant drop in incentives over the past 10 years (his rough estimate is 71% 😮). While increasing incentives could potentially attract more fraud, he argues that as more good respondents participate, the proportion of bad respondents would decrease.  Fair enough—so why don’t we increase incentives? Because it will increase the CPI and not the profit margin. 🤔 Chris Atkins at Yonder Data Solutions writes about how “the research industry is focusing too much on managing the symptoms of poor data quality rather than addressing its root causes.” I agree 100%. Our response to the data quality crisis is to build bigger armies. We need fraud detection software and QC checks, but that’s not fixing the root cause of the problem.  😐 Terence R. McCarron at OpinionRoute writes about how ‘conversion rate’ is what dictates the CPI, not the old ‘Incidence Rate.’ This matters because quality removals lower conversion rates, which is generally bad for business if you’re a sample provider. He says, “quality is not a priority that’s assured through the economic model.” Amen to that. 🙌 Links in the comments 👇 Your thoughts? 💬 #rmx #dataquality #surveys 

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Good morning data community! Okube presents a tutorial on how to use Laktory data sources. Check it out to learn how you can simplify read data from event files or tables in isolation or in the context of data pipelines. What kind of data sources would you like to be supported next? Let us know in the comments below. https://lnkd.in/e3WF2Muw 🔗

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AI: Hi there! I can answer any questions related to PLTR - Earnings call Q1 2024.Me: What are the future risksAI:Searching... What are the future risks for Palantir Technologies?The future risks for Palantir Technologies, as discussed in their Q1 2024 earnings call, can be categorized into several key areas:Societal and Cultural Risks: Alexander Karp, CEO of Palantir, highlighted the risk posed by a regressive way of thinking that he describes as corrupting and corroding institutions. This mindset, which he refers to as "woke" but critiques as a form of thin Pagan religion, is seen as a danger to society and by extension to Palantir. The concern is that if such ideologies are not openly discussed and addressed, they could negatively impact Palantir's operations and its alignment with customers.Competition and Market Positioning: While Karp expressed confidence in Palantir's unique product offerings and market position, stating that there is no real competition due to the specialized nature of their software infrastructure, this also implies an inherent risk. The company's success relies heavily on maintaining its technological edge and the unique value proposition of its products like Apollo foundry and ontology. Any failure to innovate or adapt could potentially erode its competitive advantage.Scaling Challenges: There are indications of challenges related to scaling up operations. The company acknowledges difficulties in expanding its workforce at a rate that matches the demand for its services. This limitation could hinder growth prospects if not effectively managed.Geopolitical Sensitivities: Palantir's outspoken stance on geopolitics and its defense of what it sees as Western values could pose risks in terms of public perception and relationships with international clients or governments with differing viewpoints. Such positions might lead to controversies or backlash affecting the company's global operations.Economic Headwinds in Europe: The company has identified economic stagnation in Europe, where it conducts 16% of its business, as a potential problem. With Europe gliding towards 0% GDP growth over the next couple of years, Palantir faces challenges in these markets that may affect its revenue streams from this region.In summary, while Palantir Technologies exhibits strong confidence in its product offerings and market position, it acknowledges several risks ranging from societal ideologies affecting corporate culture, challenges related to scaling operations efficiently, geopolitical sensitivities due to outspoken stances on certain issues, to economic headwinds in significant markets like Europe. Addressing these risks effectively will be crucial for sustaining growth and maintaining competitive advantage.

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Recently, a significant shift has occurred in the landscape of large language models (LLMs), marking a decisive turn towards a business-to-business (B2B) approach. Just a few weeks ago, Snowflake unveiled Artic, their groundbreaking 17 billion parameter model, tailored specifically for enterprise applications. This model stands out not only for its impressive technical capabilities in SQL generation, code completion, and logical operations but also for its bold claim as "The Best LLM for Enterprise AI." This development resonates deeply with me, reflecting a broader trend where companies like Databricks with their Mosaic initiative are emphasizing attributes like training and inference efficiency. As someone deeply immersed in this field, it's fascinating to see these specialized models like Artic and DBRX shift away from the more generalized approach of predecessors like GPT-4. The focus is now distinctly on meeting the nuanced needs of data-driven businesses, where practical tools like code automation are valued over vast general knowledge. Moreover, the move towards smaller, more cost-effective models—such as Meta's Llama3 8b—suggests a strategic pivot to accessibility and efficiency. This evolution excites me as it promises a richer diversity of models, each offering tailored capabilities that cater to specific domains of expertise. It's a thrilling time for us—founders, developers, and users—as we stand at the cusp of further innovations that promise to redefine the boundaries of what LLMs can achieve. Thank you Tomasz Tunguz for helping understand this new development.

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Very excited to showcase our new ROI and incoming insights features to help engineers manage their Databricks jobs clusters with confidence! These are features learned from the millions of core hours currently managed by Gradient and have been our most requested features from users. The insights are based on both historical trends, real time costs, as well as spark event logs, to provide a new level of information only found in Gradient. Check out the blog post to learn more! Sync Computing

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Have you ever wondered about the differences between Datafold's Data Diff and #dbt tests? Do data teams really need both? In this post, Elliot G. and Leo Folsom explain: ❓ Why dbt tests prevent some data quality issues, but not all 🤔 How the two tests answer fundamentally different data quality questions 🤝 And why having both in your CI pipeline is essential for complete data quality coverage https://lnkd.in/ggmC97hn

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Data modeling is one of the most underrated skills for data engineers right now. With DE tooling moving towards the pay-by-usage model, having inefficient data models can quickly add up. Here are the 𝘁𝗼𝗽 𝟱 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 recommended by data engineers: 1️⃣ (Paid) The Data Warehouse Toolkit: https://lnkd.in/dXCxGJKm 2️⃣ (Free) Dimensional Modeling: The Heart of the Data Warehouse: https://lnkd.in/gM2hzyzb 3️⃣ (Paid) Building a Scalable Data Warehouse with Data Vault 2.0: https://lnkd.in/gVaChiT4 4️⃣ (Paid) Modeling the Agile Data Warehouse with Data Vault: https://lnkd.in/gHuxwFuZ 5️⃣ (Free) Activity Schema Website: https://lnkd.in/gNRc_qMQ #DataEngineering #DataModeling

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Data modeling is one of the most underrated skills for data engineers right now. With DE tooling moving towards the pay-by-usage model, having inefficient data models can quickly add up. Here are the 𝘁𝗼𝗽 𝟱 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 recommended by data engineers: 1️⃣ (Paid) The Data Warehouse Toolkit: https://lnkd.in/dXCxGJKm 2️⃣ (Free) Dimensional Modeling: The Heart of the Data Warehouse: https://lnkd.in/gM2hzyzb 3️⃣ (Paid) Building a Scalable Data Warehouse with Data Vault 2.0: https://lnkd.in/gVaChiT4 4️⃣ (Paid) Modeling the Agile Data Warehouse with Data Vault: https://lnkd.in/gHuxwFuZ 5️⃣ (Free) Activity Schema Website: https://lnkd.in/gNRc_qMQ #DataEngineering #DataModeling

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👋 🛠️ Say goodbye to complex troubleshooting. With Synthia, the AI assistant built into Sisense's Compose SDK, developers get real-time guidance—no technical jargon needed. Whether you’re a seasoned coder or new to the game, Synthia makes navigating Compose SDK seamless, solving challenges and optimizing workflows faster. Start your 30-day free trial 👉 https://bit.ly/4dGzK7j #AI #SaaS #DataScience

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You know a Semantic Layer would be hugely valuable, but how do you actually build such a thing? Pro tip: Crafting a Semantic Layer is about building iterative velocity alongside accuracy, so that when your stakeholders ask about Revenue MoM grouped by Attribution Channel, you can answer instead of adding a ticket to the backlog. Start with these four steps: 1. Identify a Data Product that is impactful: Find something that is in heavy use and high value, but fairly narrow scope. Don’t start with a broad executive dashboard that shows metrics from across the company because you’re looking to optimize for migrating the smallest amount of modeling for the highest amount of impact that you can. For example, a good starting place would be a dashboard focused on Customer Acquisition Cost (CAC) that relies on a narrow set of metrics and underlying tables that are nonetheless critical for your company. 2. Catalog the models and their columns that service the Data Product, both in dbt and the BI tool, including rollups, metrics tables, and marts that support those. Pay special attention to aggregations as these will constitute metrics. 3. Melt the frozen rollups in your dbt project, as well as variations modeled in your BI tool, into Semantic Layer code. 4. Create a parallel version of your data product that points to Semantic Layer artifacts, audit, and then publish. Creating in parallel takes the pressure off, allowing you to fix any issues and publish gracefully. You’ll keep the existing Data Product as-is while swapping the clone to be supplied with data from the Semantic Layer. Dig deeper into the step-by-step process of how to ship a Semantic Layer in pieces at our link in the comments.

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