Kanwar Bhasin

“Sharing is Caring” Hi #AI Community!!! #sharingiscaring “#Knowledge Booster” 📚 Expand your knowledge base with this helpful article..Keep #Learning !! 👍 NOTE : This is NOT a paid Advertisement #Neurology #neurosciences #Robotics #artificialintelligence #dataanalytics #datascience #machinelearning #deeplearning #python #R #probability #permutations #statistics #calculus #linkedinfamily #knowledgesharing #machinelearningalgorithms #neuralnetworks #deeplearning #deeplearningai #computervision Join me on my way into an exciting world of Data/Analytics/AI 🍁Let’s Connect Now ! https://lnkd.in/dHvfKQh5

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6 things ❄ Snowflake Copilot - your  LLM-powered SQL assistant - can do today for your data workflow: 1. Explore your data. Ask open-ended questions about your data, and gain deeper insights — all without writing any SQL queries. 2. Generate SQL queries. Ask your data questions in plain English and Copilot will write SQL queries without you having to lift a finger. 3. Build complex queries. Refine suggested SQL queries conversationally by asking follow-up questions, allowing you to introduce more complexity. 4. Improve existing queries. Copilot can help explain and optimize your existing queries, resulting in cleaner, more efficient SQL queries. 5. Learn about Snowflake. Ask questions about Snowflake documentation and Copilot will help you find the relevant information and apply it to your dataset. 6. Customize responses. Customize how Copilot responds and give it access to more information by providing a set of custom instructions. #snowflake #aidatacloud #datacloud #makeitsnow #aiml #llm #codellama #mistral

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⛑️☁️ Preview on how Epic Cogito Cloud on Microsoft Fabric will be able to work with Snowflake without copying data: "This will enable our joint customers to experience bidirectional data access between Snowflake and Microsoft Fabric, with a single copy of data with OneLake in Fabric. Organizations using both platforms will be able to do so more cost-effectively, rather than building pipelines or maintaining copies of data in each platform." - Christian Kleinerman ❄️ #interoperability #datacloud #apacheiceberg #apachextable #fabric #msfabric #epic #epiccogito #cogitocloud #clarity #caboodle #nebula #epicnebula #snowflake #onelake #aidatacloud #aiml #healthcare #healthIT #hcldr #hitsm #healthtech

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The Secret to Driving AI Impact Isn't in the Algorithms – It's in the Execution. Over my 10 years in the AI/ML industry, leading teams at IBM Watson, Centene Corporation, and Datavant, I've seen one common thread that separates successful AI initiatives from failures: It’s not just about having the smartest data scientists/MLEs or the most cutting-edge algorithms – it's about bridging the gap between AI potential and business value. Here’s what I've learned about translating AI into real business impact: 1. Start with the Problem, Not the Tech Too often, I see companies enamored with the latest AI trends without a clear understanding of the business problem they aim to solve. The most successful projects I’ve led started with a well-defined pain point. AI is only effective when it’s solving a real, high-priority issue. 2. Build a Cross-Functional Team AI isn't just for data scientists. You need domain experts, product managers, and operational leaders who understand the intricacies of your industry. When I led a project that saved a healthcare company millions, it wasn’t just AI magic – it was a collaborative effort between our tech team and healthcare professionals. 3. Establish a Robust MLOps Framework Early Deploying a model isn't the finish line – it's just the beginning. One of the proudest achievements in my career was building an MLOps platform that allowed my team to monitor, iterate, and scale models efficiently. This ensured our solutions adapted as the business evolved, rather than becoming outdated and irrelevant. 4. AI Governance is Non-Negotiable Data privacy, ethical considerations, and transparency are not just buzzwords. Establishing an AI governance framework should be a top priority. When I implemented AI governance at scale, it not only safeguarded us from potential risks but also built trust with stakeholders, accelerating AI adoption across the organization. 5. Communicate the Value to Non-Technical Stakeholders One of the biggest challenges in AI leadership is getting buy-in from non-technical stakeholders. Early in my career, I struggled with this, but now I focus on framing AI outcomes in terms of revenue, cost savings, or customer experience improvements. This shift in communication led to multiple successful AI initiatives gaining executive sponsorship. 💡 Pro tip: Stop obsessing over “explainability” in purely technical terms. Instead, illustrate how the model’s insights translate into actionable business strategies. I’d love to hear your experiences – what’s the biggest challenge you’ve faced in implementing AI? How did you overcome it? #AILeadership #AIExecution #MLOps #BusinessImpact #AIProductManagement

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These data science podcasts are fantastic, keep up the good work Bill Franks! In this episode my friend & TAG Data Governance Society Board Member Nathan Ghadirifard provides perspectives on healthcare analytics. #KSU📚 #GoOwls🦉#AI🧨 #VicianaData🔥

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🚀 Introducing xLAM, a family of large action models designed to empower AI agent systems released(CC-BY-NC-4.0 license) by Salesforce AI Research. 🤖 Here's what makes XLAM special: 💪 Diverse Models: Five models, both dense and MoE architectures, ranging from 1B to 8x22B parameters, catering to various needs. 🛠️ Unified Data Pipeline: Combines cleaned & augmented agent datasets, synthetic function-calling data, and general instruction-tuning, boosting performance across environments. 🏆 Top Performance: xLAM models achieve high performance on benchmarks like AgentBench, ToolBench, and Berkeley Function-Calling Leaderboard. Key takeaways from the research: ✨ Data unification and augmentation are critical for enhancing model generalizability. ✨ Scalable data synthesis shows promise in improving agent performance. ✨ Smaller models (1B & 7B) excel for on-device deployment. ✨ Larger models (8x7B & 8x22B) tackle more complex tasks. Check out the models: https://lnkd.in/gq27fYXQ Github: https://lnkd.in/gZv5_h-r Paper: https://lnkd.in/g2iw5j46 #LLM #AIagents #OpenSource #SalesforceAI #XLAM #FunctionCalling #AIResearch #MachineLearning #DeepLearning

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Retrieving and Processing JSON Data

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Hyperscale elastic pools for Azure SQL Database helps to optimize price and performance by pooling resources like CPU, memory, and IO. They ensure strong security isolation between databases, offer superb reliability, great performance, and scalability, and provide excellent value for money.

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Revolutionizing Healthcare with Generative AI: A New Standard for Precision in Patient Care Forecasting GenHealth.ai Unveils Large Medical Model, Surpassing Industry Benchmarks by Over 14% in Healthcare Cost and Risk Prediction Highlights of the study include: - 𝐀 𝐍𝐞𝐰 𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐒𝐜𝐡𝐞𝐦𝐞 ✅ - 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐀𝐫𝐭 (𝐒𝐎𝐓𝐀) 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐧 𝐂𝐨𝐬𝐭 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 ✅ - 𝐒𝐎𝐓𝐀 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐧 𝐂𝐡𝐫𝐨𝐧𝐢𝐜 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 ✅ - 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 ✅ They've created the Large Medical Model to address a broad range of healthcare applications where LLMs and traditional analytics fall short. Their healthcare specific AI is built from the ground up to support everything from pop health analytics Definitely one to watch! Excited to see what's next for GenHealth.ai Ricky Sahu & Ethan Siegel https://lnkd.in/eNzr5KAv

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An excellent paper and must read for those tracking Data AI, Cloud, DoD, etc. Written by the leaders in Data Cloud Snowflake

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The Snowflake Arctic LLM is now available in the Azure AI Model catalog. I'm sure we'll chat about this at Epic XGM this week as lots of organizations are using Azure with Epic. The partnership triforce is forming! 🔺 #azureAI #snowflake #snowflakearctic #llm #aiml #epic #epicxgm #datacloud #healthcareAI #healthcareLLM

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AI in finance as in other domains, it is promising tool, but has yet to be proved. https://lnkd.in/dPv6mKDa #ai #artificicalinteligence #finance #machinelearning #datascience

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TED Conferences are my jam! I watch them all the time. Here are some great ones that dive into AI. I look forward to catching them all ✨ P.s. I’ll shamelessly plug mine in the comments that talked about how we can innovate in healthcare by reimagining collaboration. It’s called “Did Disney Just Save Healthcare?!? Imagine This…” I think one of my future post will dive into some more healthcare innovation related ones. Does anyone have any suggestions? #ai #tedx #tedtalk #collaboration #innovation

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There’s no AI without data. Join us at #CloudWorld and learn how to kickstart and expand your #AI initiatives with our Database, Autonomous Database, and Exadata. https://lnkd.in/g74ugvaA

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Maximize your data. Minimize lost opportunities. Opportunity costs from data aren't just numbers on a spreadsheet — they're the potential growth and innovation opportunities we miss when data isn't fully harnessed to maximize business performance. In today's competitive landscape, data is a goldmine, but only if we leverage it correctly. Imagine businesses struggling with traditional data management techniques and over investing in advanced cloud native & AI enabled Data management technologies. The result? Lost insights due to poorly managed data that could transform business decisions, enhance customer experiences, and streamline operations! Companies skipping proactive or timely investments in advanced data management and data governance might save short-term costs, but the price of inaccurate data is far higher — leading to poor decisions, hefty penalties, data breaches, lost competitive edge and more. As leaders and architects, understanding and measuring the opportunity costs is crucial in decision making. It's about ensuring every byte of data works towards driving innovation, efficiency, profitability and future-proofing businesses. Invest in the right technologies, talent, and processes to maximize full potential of your data and stay ahead in the game! Thereby, minimize possibilities of losing opportunities. Let’s ensure our data is working for us, not against us. #DataStrategy #DataManagement #DataGovernance #EnterpriseArchitecture

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