The AI Furnace ????的动态

?? Meet Dataiku – the leading platform for Everyday AI. It systemizes the use of data for exceptional business results. By making the use of data and #AI an everyday behavior, Dataiku unlocks the creativity within individual employees to power collective success at companies of all sizes and across all industries. ?? Founded in 2013 in France by Florian Douetteau, Marc Batty ??, Clément Stenac, and Thomas Cabrol, Dataiku offers a platform that empowers data scientists and employees to utilize unstructured data more effectively. Their software facilitates collaboration across teams in various data processes, from data #scrubbing and wrangling to analysis. Florian explains, “We equip analysts, data scientists, and employees to enrich data, rather than creating a solution from scratch. Being French, we're all about efficiency and doing as little as possible.” Dataiku achieved unicorn status in 2019 and is backed by a renowned set of investors and partners including CapitalG, Tiger Global Management, and Serena, With a client base of over 600, including 200 of the Forbes Global 2000, and a dedicated team of over 1,000 professionals, Dataiku is leading the charge in helping enterprises harness the full potential of data, analytics, AI, and #GenerativeAI. ?? Discover their #OpenRoles today: https://lnkd.in/ejrdSntQ #DataAnalytics #DataScience #EnterpriseSoftware #MachineLearning

Simplest Explanation of Data Pipelines.

Outstanding, easy to understand view of the complexity of data flows in an organisation. And why it's easy to imagine that getting clean, useful data is not simple. Getting clean data for use in analyses, dashboards, management reporting and AI , and keeping it aligned as businesses and systems change over time is hard. Doing this well needs skills. It's good for companies to have data scientists but it's even better if individuals across the companies understand the complexities and how to manage them. These won't start projects bound to break down and will structure projects that will succeed. There are many free courses on data cleaning on YouTube and Coursera. Also known as data wrangling and data hygiene. You'll be amazed how much more effective you'll become after a short course. #treasury #treasurymanagement #corporatetreasury #procurement #datascience #datascientist

A model is nothing without data, a model is nothing without quality data. In real world, data normally lacks quality but we can improve and enhance our databases with feature engineering. Always perceive in first place good data quality and then yes, you can make your wizardry and deliver your AI product ?? #data #ai #dataquality

Are Data Analytics dead..? Many will say “no way!” and will be totally right in many aspects – the data analytics are the foundation of today’s business success, of intelligent and agile use of data to drive true insights, and much more. Certainly it would be hard to find any company today with digital assets that’s wouldn’t do some sort of data analytics. However, the provoking keyword is “today”. The motion of “AI-everything” is not slowing down and rather expands to next level of expectations. During the launch of “The European AI conference” by Google earlier today I’ve noticed appearance of concepts like “Digital Twin for Culture” that could be AI driven (obviously!) and make a tremendous breakthrough in the way how we learn and communicate. Never the less, back to question about where does the Data Analytics stands in coming days, weeks, and months – would that be a sort of “overridden” and outplaced eventually by AI, with AI coming at the first place, or would it be marginally democratized by upcoming integration of natural language interface into BI (aka AI-BI), or would it remain what it is today and what it was for years – a cornerstone of data processing and representation for business use, an?intelligent source of human-made decisions, and as a concrete example of the value of data, - above and beyond the AI hype..? Frankly, I don’t know. But I would love to learn about latest trends in this space and how data analytics and AI stands together for a better tomorrow. I’m attending the Data Analytics Summit in Berlin by Uniglobal , June 13-14th and looking forward to actively engage and learn from the professional community. See you soon! #genai #dataanalytics ?#ungdas ?????

?? ????????????????: ?? ?????????????? ???????? ?????? ???????????????? ????????????, ????-???????????? ???????? ?????????????????? ?? GraphRAG represents a major innovation in Retrieval-Augmented Generation (RAG) by integrating knowledge graphs to enhance AI’s ability to handle complex, narrative-rich data. ?? ???????? ???? ????????????????? ? Enhanced RAG: Combines RAG with knowledge graphs, organizing data into interconnected nodes and relationships. ? Better Retrieval: Offers more accurate, contextually relevant answers, especially for complex queries. ?? ?????? ???? ??????????????: ? Overcomes RAG Limitations: Tackles challenges like multi-hop reasoning and linking disparate information. ? Industry Applications: Ideal for social media analysis, news synthesis, and enterprise data management. ?? ?????? ???? ??????????: ? Knowledge Graphs: Extracts entities, relationships, and claims to build a dynamic knowledge graph. ? Advanced Querying: Uses these graphs for more comprehensive, accurate responses with clear provenance. ?? ????????-?????????? ????????????: ? Improved Accuracy: Connects related information for more precise answers. ? Broad Applications: Revolutionizes AI-driven insights in various industries. ?? ?????? ???????????????? ????????????: GraphRAG is setting new standards in data discovery, bridging the gap between raw data and meaningful insights. But with the rise of large-context LLMs, is this the end for all RAG approaches? #AI #GraphRAG #MicrosoftResearch

???????? ???? ??????????-???????? ?????? ???????? ?????? ???????????????? ?????????????????????? ???? ???????? ?????????????????? ??????'???? ???????? ??????! ?? In today’s AI-driven world, robust data pipelines aren't just a necessity — they're the FUEL that powers everything. ? Without them, AI is just a fancy idea with no real impact. Data pipelines are the backbone of modern data-driven businesses. And they automate the process of collecting, organizing, and transforming data. ...?????? ???????????????? ???? ?????????????????? ???????? ???????????????? ???? ?????????? ????????! Here are the key stages to consider: 1?? ???????? ??????????????: Collect raw data from various sources. 2?? ???????? ??????????????: Ingesting the right and trusted data. 3?? ???????? ????????: Store the raw data in a highly accessible format. 4?? ??????????????????????/??????????????????????: Process and transform the data. 5?? ???????? ??????????????????: Store the processed data for specific purposes. 6?? ???????? ??????????????: Make the data available for analysis and decision-making. Nailing these stages is the formula for creating a data pipeline that goes beyond basic functionality and fully supports your business goals! #data #ai #datastrategy #datascience #aianddata Image from: Semantix

?? Seeking to elevate your data analytics prowess? Prepare for a paradigm shift with the latest advancements in AI technology! Explore the dynamic landscape of data analysis with trailblazing platforms such as DataChat and Pecan AI. These innovative tools redefine the boundaries of efficiency and intelligence, offering a gateway to unparalleled insights and strategic decision-making. Are you poised to embark on a transformative journey? Join the league of forward-thinkers harnessing the power of AI to revolutionize data analytics. Let's embark on this exhilarating expedition towards data-driven excellence! 1. Unrivaled Accuracy: OpenAI platforms leverage advanced machine learning algorithms to deliver pinpoint accuracy in data analysis, ensuring you make informed decisions based on reliable insights. 2. Speed and Efficiency: Say goodbye to hours of manual data processing! With OpenAI, tasks that used to take days can now be completed in minutes, allowing you to streamline your workflow and focus on what truly matters. 3. Scalability: Whether you're dealing with small datasets or massive volumes of information, OpenAI platforms are designed to scale effortlessly, adapting to your needs as your business grows. 4. Insightful Predictions: Predictive analytics becomes a breeze with OpenAI, as these platforms can forecast trends, identify patterns, and anticipate future outcomes with unparalleled precision. 5. Enhanced Decision Making: Empower your decision-making process with AI-driven insights that go beyond surface-level observations. OpenAI platforms uncover hidden correlations and insights that human analysis might overlook, enabling you to make strategic choices with confidence. In a world where data reigns supreme, harnessing the power of AI is not just an option - it's a necessity. #DataAnalytics #AI #OpenAI #DataScience #MachineLearning #ArtificialIntelligence #Innovation #RevolutionizeDataAnalysis

Introducing Lervis Enterprise – Transforming Data into Actionable Insights! In today’s data-driven world, many businesses struggle to extract meaningful insights from their vast data.?Lervis Enterprise?changes the game by allowing anyone to interact with data using?simple, natural language—no need for SQL expertise! Powered by?Generative AI?and?Retrieval-Augmented Generation (RAG), Lervis translates your queries into SQL, retrieves the data, and delivers?instant visualizations. It connects seamlessly to both structured and unstructured data sources like CSVs, PDFs, and internal documents. With features like?Human-in-the-Loop validation,?few-shot learning?for adaptability, and?real-time analytics, Lervis Enterprise democratizes data access, empowering everyone in your organization to make smarter, faster decisions. It’s not just a query tool—it’s a full platform for business insights, from generating marketing content to tracking the impact of AI-driven decisions.?Lervis Enterprise?redefines how businesses unlock the power of their data. #AI #DataInsights #BusinessIntelligence #LervisEnterprise #GenerativeAI #DataTransformation #GenAI #RAG Link: https://lnkd.in/gUBy8eTJ CC: UTKARSH Tiwari, Jahanve Poluru & Siwani Karna

?? Model-Centric vs. Data-Centric Approaches in Data Science ?? When it comes to creating high-performing data solutions, there’s often a choice to be made: do we focus on refining the model or enhancing the data itself? ?? Model-Centric: This approach revolves around optimizing algorithms, fine-tuning parameters, and exploring complex architectures to improve accuracy. It’s all about pushing the limits of what the model can achieve. ?? Data-Centric: A growing approach that focuses on the quality and relevance of data. Here, we clean, label, and balance datasets to create a strong foundation, often yielding high-performing models with reduced complexity. ?? Which approach have you found most impactful in your projects? Let’s discuss! Has focusing on data quality led to better insights, or do you still prioritize model improvements? Share your thoughts and experiences below! ?? #DataScience #AI #MachineLearning #DataQuality #ModelOptimization #LinkedInCommunity #WyTaSoft #TheLeagueOfExtraordinaryDataEngineers