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The 2024 MAD (ML, AI and Data) landscape by Matt Turck and Aman Kabeer (Thanks!!) just released 📣 📣 and now that it's released, I'm excited to share Dataloop AI journey from a leading data annotation tool to an end-to-end AI platform for enterprises. This strategic pivot is not just a response to the evolving market demands but a forward-looking vision to redefine how businesses will leverage AI technologies. 𝐓𝐡𝐞 𝐩𝐮𝐬𝐡 𝐟𝐨𝐫 𝐂𝐡𝐚𝐧𝐠𝐞 ↗ Recently, the AI industry has witnessed major shifts, most notably in the availability of GenAI models (LLM, LMM, LXM) as they become completely free for commercial use and the maturity of enterprise AI workflows, which are currently pieced together from dozens of AI/ML tools, becomes unsustainable to a growing demand for integrated AI solutions. Our observations, backed by conversations with industry leaders and our valued customers, highlighted a clear trend: The need for holistic, data-centric AI platforms that cater to the entire lifecycle of AI development, from data prep and management to model deployment and scaling. 𝗘𝗺𝗯𝗿𝗮𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 🎬 🎶 📖 Dataloop AI pivot is driven by the raising of the critical role that unstructured data plays in the AI landscape. Unlike structured data, unstructured data – is the fuel for GenAI models, offering insights and capabilities. Dataloop has always excelled at managing and processing unstructured data, and now with our new platform leverages this expertise to provide the best unstructured data management and data pipeline solutions available. 𝗔 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲, 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 🔛 Dataloop's new platform is a testament to our commitment to excellence. We're not just addressing the immediate needs of our customers but setting the stage for the future of AI development. Our platform offers a seamless, integrated experience that covers every aspect of AI workflow, making AI development accessible, scalable, and efficient for enterprise. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗣𝗶𝘃𝗼𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 ⏩ This strategic shift is more than just a change in our product offerings; it's a reflection of our deep understanding of the AI industry's trajectory and staying ahead of the curve. By focusing on comprehensive solutions that address the entire AI development cycle, Dataloop is uniquely positioned to empower businesses to unlock the full potential of AI technologies. 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗔𝗵𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝗘𝘅𝗰𝗶𝘁𝗲𝗺𝗲𝗻𝘁 😎 The journey ahead is incredibly exciting. We're not just participating in the AI revolution; we're leading it. Our end-to-end AI platform is designed to meet the dynamic needs of the market, ensuring that our clients have the tools and support they need to succeed in the rapidly evolving GenAI landscape. visit www.dataloop.ai #AI #DataAnnotation #GenerativeAI #UnstructuredData #Innovation #TechLeadership #StrategicPivot #datapipeline

Avi lays out our platform journey, how we address market shifts and why it matters!

Are you really using your staff to best of their ability? Are they looking for data in Documents, or worse pictures and emails. People still think of emails and contracts and CRM and ERP as separate entities. It is possible to update your CRM as an email is sent or received. Or your Project Management tool Or check what your client has signed for and does it agree with what you have in your forecast. If you think ChatGPT is AI, you are very WRONG. ChatGPT has been a huge advertising billboard for AI but it is only a really small part of the new landscape. In face, many of us think AI Chat bots have already seen their best days. They are becoming commodities - choose the one you like; they are all brilliant or awful. Depends on what you ask and how. But Enterprise AI is just starting Use cases include anything where humans are overloaded with manual tasks Loss adjusters in Insurance Purchase Managers in Finance Project Managers in any environment. Quality Assurance Processes They are all ripe for change If you are having to read emails, documents, compare prices externally to find alternatives, review history, compare prices across your organisation. Look for patterns Review weaknesses and strengths Compare offices for best practice Automation tools include Large Language Models and Generative AI tools are ready for you to use NOW. The error levels are frighteningly low; much lower than humans On your data to find the insights that you need. If you are bored of AI, BEWARE Wake up, smell the coffee, or you WILL miss the train. I have been in business for too many years and I have NEVER seen anything like it. Understand what you need to do BEFORE you buy any new technology It is easy to waste a ton of money. But it is also easy to make great strides forward Very quickly On Reasonable budgets. Don't get left behind. Do not get left scrabbling to catch up. If you want to know more, do ask. I will NOT try and sell you any technology. I will help you think over your next steps What you do with that advice is your call. It is a very wild world - beware the snake oil sales-peeps Who promise the world, charge a fortune and deliver very little. Look for small steps that deliver value. Projects can pay for themselves Do it right and you can get ROI in a matter of months, even weeks https://dtp.im/ai-call

🚀 Exciting insights on the Future of Data Platforms with Generative AI from Select Star and Brooklyn Data Co. (a Velir company)! In a candid conversation between Select Star's founder and CEO, Shinji Kim, and Scott Breitenother, Chief Data Officer at Velir and founder of Brooklyn Data, they explore the potential of AI in data analytics. • The Path to AI-driven Business Intelligence The discussion delves into the evolving role of AI in data, predicting that AI will soon be capable of providing business intelligence without human-generated SQL. While current AI models face challenges in delivering reliable and high-quality data, Select Star's innovative approach leverages existing queries and dashboards to guide users in the right direction. 💡 The Role of AI in Data Security As data democratization takes hold, AI tools can help businesses strike a balance between granting access to data and protecting it. With role-based and policy-based access control, AI can ensure data security while still empowering non-data teams. • AI's Role in Regulatory Compliance Navigating the complex landscape of privacy regulations like CCPA and GDPR can be intimidating for medium-sized businesses. However, AI-powered adaptive governance tools can simplify this process by helping businesses understand requirements and ensure compliance. ⚙ The Modern Data Stack and AI Innovation The integration of AI into SaaS tools is expected to drive innovation in the data space. Established vendors with research and development teams and customer trust are likely to lead the way by incorporating AI features into their existing offerings. • The Future of Generative AI in Data We are on the cusp of a future where AI's capabilities extend beyond task-oriented assistance. With the advent of artificial generalized intelligence (AGI), AI tools will become advisors, offering context and suggesting tasks to achieve desired outcomes. 🌟 Ready or Not, the Future is Now While AI's penetration in the data industry may appear slow due to the complexity and importance of data accuracy, the transformative impact of AI is undeniable. As AI-powered data products evolve, businesses will enjoy informed decision-making at the click of a button. Learn more at https://lnkd.in/gSDc4F7K

🌟 Data drives decisions, and its quality drives success! In the AI-driven era, pristine data isn't a luxury—it's essential. Join the conversation with Qlik experts Andrew (Drew) Clarke Gina Rosenthal Joey D'Antoni & Stephen Foskett dive deep into the imperative of high-quality data. 🔍 Discover strategies to integrate data with AI models effectively and explore how robust data practices can accelerate your AI initiatives. Don't miss out on this insightful discussion that can transform your thinking and approach to data in a AI world. #AI #DataQuality #Technology #Innovation #Leadership

🚨 Snowflake Boosts Data Quality for AI with Metaplane Investment 🚀 Original Source: https://lnkd.in/damWhKu2 📝 Intro: Snowflake's strategic investment in Metaplane highlights the critical role of data quality in AI development. As businesses increasingly rely on AI to drive decision-making and innovation, ensuring the accuracy and reliability of the underlying data becomes paramount. This partnership aims to address the data quality challenges that have been hindering AI progress. 🔍 Multi-dimensional view: - Rational: Improved data quality leads to more accurate AI models and better business outcomes - Emotional: Frustration with AI failures due to poor data quality; hope for more reliable AI solutions - Social/political: Pressure to adopt AI responsibly; need for transparency and trust in AI-driven decisions ❓ Questions for you: 1. How confident are you in the quality of the data powering your AI initiatives? 2. What steps are you taking to ensure data accuracy, completeness, and consistency? 3. How are you balancing the push for AI adoption with the need for responsible and trustworthy AI? 💡 Strategic reflections: Many companies have rushed to implement AI without adequately addressing the foundational issue of data quality. This can lead to AI systems that make flawed decisions, erode trust, and fail to deliver the promised value. It's time to reassess whether your AI investments are built on a solid data foundation. 🎯 What would matter to your customer: 1. Prioritize transparency and explainability in your AI-driven offerings to build trust 2. Emphasize the steps you're taking to ensure data quality and responsible AI deployment 3. Highlight the tangible benefits and improved outcomes that reliable AI can deliver 🔧 Reframe your value prop: 1. "Trustworthy AI for Better Business Decisions": We prioritize data quality to ensure accurate, reliable, and responsible AI solutions that clients can depend on for enhanced operational efficiency, customer satisfaction, and profitability. 2. "AI-Driven Insights You Can Rely On": Our AI offerings are powered by meticulously curated, high-quality data, enabling clients to make confident, data-driven decisions that optimize strategy and innovation. ✅ Your turn: Take a deep dive into your data landscape and assess the quality of the data fueling your AI initiatives. Work with your team to identify and address any data quality issues, and rebuild your AI value proposition around the promise of trustworthy, reliable, and responsible AI that delivers real business value. #DataQuality #TrustworthyAI #ResponsibleAI #DataDrivenDecisions #SnowflakeMetaplane

New Perspectives on AI Implementation in Business -  The Data-First Approach I recently came across an insightful post by Oliver Molander, who used a compelling analogy, "Separating your AI Strategy from your Data Strategy is like separating the car engine from the car body." I was reminded how true this was in a recent discussion I had with a CX consultant. The question that arises in AI adoption is this: should businesses prioritise crafting a business case first, or is preparing and optimising data more critical? 🔍 Why Data First? 👉 Readiness for AI - in a world where AI is an inevitable part of business evolution, having your data cleansed and labelled ensures you are AI-ready, whatever your eventual use case might be.     👉 Flexibility and Agility - with ready-to-use data, your organisation can quickly pivot and adapt to new AI opportunities or needs as they arise, without the lag of preparing data from scratch. 👉 Enhanced Data Culture - emphasising data preparedness fosters a culture that values data integrity and readiness, an indispensable asset in the modern data-driven landscape. 👉 Cost and Time Efficiency - addressing data issues upfront can reduce both the time and cost associated with implementing AI solutions later on. 💡 The Business Case will Follow Yes, a business case is crucial – it provides direction and justification for AI initiatives. Preparing your data in advance equips you to swiftly capitalise on AI opportunities, tailoring the business case to the specific application at hand. At OmniaTeam, we advocate for a proactive approach. As an innovator in Digital Workspace Solutions, we understand the importance of having a robust, AI-ready data infrastructure. By preparing your data early, you're not just ready for AI - you're setting a foundation for innovation and future-proofing your business. 💬 Your Thoughts? I’d love to hear from other professionals – do you believe in a data-first approach or see more value in developing a business case first? Let's discuss the best strategies to embrace AI in our business processes! #AIReady #DigitalTransformation  #AIIntegration #DataStrategy #FutureOfWork #OmniaTeam

80% of enterprise #proprietarydata is unstructured. How can you make use of it for game-changing AI? Try leveraging data transformation engines like Conveyer to merge your #structureddata and #unstructureddata into AI-ready data repositories. Learn more: https://lnkd.in/et6swze3

🤔 𝐇𝐨𝐰 𝐂𝐚𝐧 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐀𝐈'𝐬 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥? The latest innovation from Microsoft, GraphRAG, is set to revolutionize how we use AI for data retrieval and generation. Here’s why it’s a game-changer: 🧠 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆? GraphRAG stands for Graph Retrieval-Augmented Generation. It's an advanced version of traditional RAG systems designed to provide more accurate and context-rich responses from AI models by leveraging knowledge graphs. 🔄 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Traditional RAG: When you ask a question, it performs a semantic search to pull relevant information and feeds it to the language model. GraphRAG: Goes a step further by extracting entities and their relationships from the data, allowing the model to understand and provide more nuanced and accurate answers. 🌐 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬: Data Chunking: Like traditional RAG, GraphRAG divides data into chunks but also extracts useful entities and their relationships. Semantic and Graph Search: Combines semantic search with graph-based context to enhance the relevance and quality of responses. 📈 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Private Datasets: Ideal for businesses with extensive private datasets, allowing for detailed and accurate data retrieval. Enhanced Q&A: Provides high-quality, summarized answers by understanding the relationships between different data points, making it useful for customer support, research, and more. 💡 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: Entity Summarization: Automatically summarizes entities and their relationships. Community Summarization: Uses pre-existing community relationships to add more context and meaning to the data. Topic Detection: Identifies and organizes data around specific topics for better insight. 🛠️ 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆: Setup: Install GraphRAG and initialize your project with simple commands. Integration: Easily integrate with models like GPT-4 or OLama by configuring API settings. Data Indexing: Index your documents to create a detailed knowledge graph. Query Execution: Perform both global and local searches to get detailed or focused answers from your indexed data. Microsoft’s GraphRAG system not only improves the quality of AI responses but also makes it easier to manage and retrieve complex data. This system is poised to take AI data processing and retrieval to the next level. 🔗 How do you see GraphRAG impacting your data retrieval and AI projects? Are you ready to implement this advanced system for better accuracy and insights? #AI #DataRetrieval #GraphRAG #Microsoft #TechnologyInnovation

💡 In the digital business, data holds the answers, but interpreting that data isn't known for being simple or accessible. AI is changing that. Whereas traditional data analysis methods have struggled to keep pace with the complexity and diversity of modern data sets, we've been experimenting with new AI approaches that change the game. Conversational Data Intelligence (CDI) - is our term for the revolutionary approach to data analysis that enables our partners to draw more meaningful insights from diverse data sets to gain a competitive advantage and deliver more value to their customers. Learn about the potential of CDI as well as the limitations of traditional data analysis in our latest blog post: https://lnkd.in/gKZ5BGRr Shout out to our Head of AI, Kevin Rohling, for leading the charge on custom Conversational Data Intelligence systems for our partners! #DataIntelligence #BusinessAnalytics #DataDriven