Vishal Ahire

🔑 𝗙𝗿𝗼𝗺 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗗𝗼𝗺𝗶𝗻𝗮𝗻𝗰𝗲: 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗔𝗜 & 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 In today's rapidly evolving digital landscape, leveraging 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜) 𝗮𝗻𝗱 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗔𝗔) is no longer a luxury—it's a necessity. 🌐 🌟 𝗧𝗵𝗲 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗝𝗼𝘂𝗿𝗻𝗲𝘆: 𝗙𝗿𝗼𝗺 𝗗𝗼𝗿𝗺𝗮𝗻𝘁 𝘁𝗼 𝗗𝗼𝗺𝗶𝗻𝗮𝗻𝘁: 🛤️ 𝗗𝗼𝗿𝗺𝗮𝗻𝘁 – 𝗡𝗼 𝗔𝗜/𝗔𝗔 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘆𝗲𝘁? Consider this the starting line. Your data has untapped potential waiting to be unlocked. 📊 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 – Move from simple reporting to understanding 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝗯𝗮𝘀𝗶𝗰 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀. 🧠 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 – Delve deeper into 𝘄𝗵𝘆 𝗶𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. Uncover the root causes and trends. 🔮 𝗖𝗼𝗿𝗲 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 & 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 – 𝗣𝗿𝗲𝗱𝗶𝗰𝘁 𝘄𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗵𝗮𝗽𝗽𝗲𝗻 𝗻𝗲𝘅𝘁 using statistical models and machine learning. Stay ahead of the curve. 🛠️ 𝗣𝗿𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 – Empower your organization with AI-driven insights that tell you 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗱𝗼 𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀. 🚀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 – The future is here. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 with cutting-edge platforms that ensure precision, scalability, and speed. 💡 𝗪𝗵𝘆 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗠𝘂𝘀𝘁 𝗖𝗮𝗿𝗲: 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲: Companies at the forefront of the AI/AA maturity curve outperform their peers by turning data into actionable, future-focused strategies. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: As you ascend the maturity curve, decision-making becomes not only faster but also more informed and automated, freeing up valuable resources for innovation. 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗲𝗱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻: The highest levels of maturity foster a culture of continuous improvement, critical for maintaining relevance and leadership in your industry. 𝗔𝘀 𝘁𝗼𝗽 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀, 𝘆𝗼𝘂𝗿 𝗿𝗼𝗹𝗲 𝗶𝗻 𝗰𝗵𝗮𝗺𝗽𝗶𝗼𝗻𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹. 𝗧𝗵𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝗹𝗲𝗮𝗱 𝘁𝗵𝗶𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘄—𝘀𝘁𝗲𝗽 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝘀𝘂𝗰𝗰𝗲𝘀𝘀. 🏆 Post Inspiration: Sunil David Raja Shanmugam Shankar Venugopal Murali Sundaram #AI #AdvancedAnalytics #ExecutiveLeadership #DigitalTransformation #StrategicPlanning #BusinessStrategy #Innovation #DataDrivenDecisionMaking #MachineLearning #FutureOfBusiness

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Excited to share that Vijay K. and I collaborated on a guide for Active-Active Replication on Amazon RDS for MySQL! This comprehensive resource is now available on AWS Solutions https://lnkd.in/d2nH2NH3 #aws #amazonrds #mysql #highavailability #disasterrecovery #rds #mysql

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🎉 300 𝗦𝘁𝗮𝗿𝘀 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯! 𝗧𝗵𝗮𝗻𝗸 𝗬𝗼𝘂! 🌟  I’m thrilled to share that the 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 has reached 300 𝘀𝘁𝗮𝗿𝘀 on GitHub! 🚀 This is a significant milestone and a testament to the incredible support and enthusiasm from the developer community.  💡 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿?  It’s a flexible and extensible framework for managing multiple AI agents and handling complex conversations. Whether you’re integrating agents like 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸, 𝗔𝗺𝗮𝘇𝗼𝗻 𝗟𝗲𝘅, 𝗢𝗽𝗲𝗻𝗔𝗜, 𝗟𝗹𝗮𝗺𝗮 𝗔𝗴𝗲𝗻𝘁𝘀, or others, the framework provides adaptability for a wide range of use cases.  📅 𝗥𝗲𝗹𝗲𝗮𝘀𝗲 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲:  🔹 𝗝𝘂𝗹𝘆: Launched the framework in TypeScript.  🔹 𝗔𝘂𝗴𝘂𝘀𝘁: Released the Python version, making the framework accessible to an even broader audience.  ✨ 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗜𝗻𝗰𝗹𝘂𝗱𝗲:  ✅ 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁: Amazon Bedrock, Amazon Lex, OpenAI, LLama Agents, and more.  ✅ 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗻𝘁 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Dynamically route queries to the best agent.  ✅ 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗮𝗻𝗱 𝗻𝗼𝗻-𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀: Flexibility for real-time and batch workflows.  ✅ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Maintain coherent multi-turn interactions.  ✅ 𝗘𝘅𝘁𝗲𝗻𝘀𝗶𝗯𝗹𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Easily integrate custom agents.  🚀 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 & 𝗤𝘂𝗶𝗰𝗸 𝗦𝘁𝗮𝗿𝘁: Get hands-on experience with the Multi-Agent Orchestrator through our diverse set of examples:  🔹 𝗥𝗲𝗮𝗱𝘆-𝘁𝗼-𝗿𝘂𝗻 𝗦𝗰𝗿𝗶𝗽𝘁𝘀: Start locally with standalone scripts in both Python and TypeScript.  🔹 𝗗𝗲𝗺𝗼 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀:  - Chat Demo App: Specialized agents for travel, weather, math, and health.   - E-commerce Support Simulator: AI-powered customer support with automation and human-in-the-loop capabilities.  🔹 𝗦𝗮𝗺𝗽𝗹𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Explore a variety of projects, including chat interfaces, AI-powered customer support systems, FastAPI integrations, and tools for converting natural language into structured data—all available in the examples folder.  Anthony Bernabeu and I are the co-authors and active maintainers of this project. We’re deeply committed to its growth and ensuring it continues to deliver value to the community.  🙏 𝗧𝗵𝗮𝗻𝗸 𝗬𝗼𝘂! To everyone who has contributed, supported, or provided feedback—you’ve made this journey incredibly rewarding.  ✨ 𝗜 𝗰𝗮𝗻’𝘁 𝘄𝗮𝗶𝘁 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲: 1000 𝘀𝘁𝗮𝗿𝘀! 🌟  Let’s make it happen together! If you haven’t already, check it out, give it a ⭐️, and let me know how you’re using the framework or what use cases you’d love to see.  https://lnkd.in/gcfSYJHM #opensource #AI #GitHub #MachineLearning #MultiAgentSystems #AmazonBedrock #OpenAI #multiagentorchestrator

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💡 𝗖# 𝐓𝐢𝐩 - 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗖𝗼𝗻𝘀𝘁 𝗮𝗻𝗱 𝗥𝗲𝗮𝗱𝗼𝗻𝗹𝘆 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀 🔥 𝗰𝗼𝗻𝘀𝘁 and 𝗿𝗲𝗮𝗱𝗼𝗻𝗹𝘆 in 𝗖# share the common feature of preventing variables from being modified after initialization, but they have some key differences. 🔷 𝗰𝗼𝗻𝘀𝘁 ➤ Constants are declared with the 𝗰𝗼𝗻𝘀𝘁 modifier. ➤ Defines a 𝗰𝗼𝗺𝗽𝗶𝗹𝗲-𝘁𝗶𝗺𝗲 constant. Its value is known and fixed at compile time and cannot be changed at runtime. ➤ Must be declared with an initializer and 𝗰𝗼𝗻𝘀𝘁 members are implicitly static. ➤ Can only be used with basic types like integers, booleans, and strings. User-defined types, including classes, structs, and arrays, cannot be 𝗰𝗼𝗻𝘀𝘁. 🔷 𝗿𝗲𝗮𝗱𝗼𝗻𝗹𝘆 ➤ Readonly are declared with the 𝗿𝗲𝗮𝗱𝗼𝗻𝗹𝘆 modifier. ➤ Defines a 𝗿𝘂𝗻𝘁𝗶𝗺𝗲 constant. Its value can be assigned during initialization or in the constructor, but cannot be modified after that. ➤ Can be declared with or without an initializer and can be static or non-static. ➤ Can be used with any type, including reference types. ❓What are your thoughts on 𝗰𝗼𝗻𝘀𝘁 and 𝗿𝗲𝗮𝗱𝗼𝗻𝗹𝘆? Comment below👇 ♻️ If this is useful, 𝙧𝙚𝙥𝙤𝙨𝙩 to spread the knowledge. 👉 Please follow me (Poorna Soysa) and click the notification bell icon (🔔)on my profile to receive notifications for all my upcoming posts. 𝗧𝗵𝗮𝗻𝗸 𝘆𝗼𝘂 𝗳𝗼𝗿 𝗿𝗲𝗮𝗱𝗶𝗻𝗴! #DotNET #CSharp #DotNETDevelopers #Programming

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📊 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐥 𝐭𝐨 𝐄𝐯𝐞𝐫𝐲 𝐒𝐭𝐚𝐠𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 𝐢𝐬 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 𝐒𝐭𝐞𝐰𝐚𝐫𝐝𝐬𝐡𝐢𝐩 𝐓𝐡𝐞 𝐝𝐚𝐭𝐚 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 𝐫𝐞𝐟𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐧𝐮𝐦𝐞𝐫𝐨𝐮𝐬 𝐩𝐡𝐚𝐬𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐚𝐬 𝐢𝐭 𝐢𝐬 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐞𝐝, 𝐜𝐮𝐫𝐚𝐭𝐞𝐝, 𝐚𝐧𝐚𝐥𝐲𝐬𝐞𝐝, 𝐮𝐬𝐞𝐝, 𝐝𝐞𝐜𝐨𝐦𝐦𝐢𝐬𝐬𝐢𝐨𝐧𝐞𝐝, and iteratively reviewed within the larger AI project lifecycle to ensure accurate, secure, and robust performance of an AI/ML system. 𝐒𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞: 📝 𝐃𝐚𝐭𝐚 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 - Strategically determine how data will address project goals. 🌐 𝐃𝐚𝐭𝐚 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 - Identify and collect new or existing data. 📊 𝐃𝐚𝐭𝐚 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐨𝐫 𝐏𝐫𝐨𝐜𝐮𝐫𝐞𝐦𝐞𝐧𝐭 - Collect structured, semi-structured, and unstructured data from various sources. 🧹 𝐃𝐚𝐭𝐚 𝐂𝐮𝐫𝐚𝐭𝐢𝐨𝐧 - Select, clean, and organize data for AI/ML applications. 🔍 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 - Explore data through visualization and summary statistics. 🔧 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 & 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 - Clean, normalize, and transform data into features for model training. ⚙️ 𝐃𝐚𝐭𝐚 𝐔𝐬𝐞 - Apply the pre-processed data in AI/ML models. 🗄️️ 𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 - Maintain data for current and future use. 🗑️ 𝐃𝐚𝐭𝐚 𝐃𝐞𝐜𝐨𝐦𝐦𝐢𝐬𝐬𝐢𝐨𝐧𝐢𝐧𝐠 - Securely remove data from systems to ensure it is no longer in use. 🔐 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 - Exchange data between systems or organizations securely. 🚨 𝐃𝐚𝐭𝐚 𝐋𝐞𝐚𝐤 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧 - Address data breaches immediately. ♻️ 𝐃𝐚𝐭𝐚 𝐑𝐞𝐮𝐬𝐞 - Repurpose existing data for new projects or research. Source: National Institute of Standards and Technology (NIST) Post Inspiration: Jeff Winter Efficient data management ensures the success and integrity of AI projects, from planning to execution and beyond. #AI #DataManagement #MachineLearning #BigData #DataScience #TechInnovation #DataSecurity #AIProjects #governance

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As our applications evolve, so must our approach to data storage. Let's examine key factors in selecting the right database for advanced use cases. Also, Join me for an upcoming Free webinar - RSVP here : https://brij.guru/ai 𝗪𝗲'𝗹𝗹 𝗰𝗼𝘃𝗲𝗿: • Fundamentals of database branching • Practical implementation strategies • How to leverage this technique for AI applications 1. Object-Oriented DBs: Consider for complex object models where relational mapping becomes inefficient. Useful in CAD/CAM systems or scientific applications. 2. Time-Series DBs: Crucial for IoT and monitoring at scale. Look into options like InfluxDB or TimescaleDB for high-write, time-indexed data. 3. Graph DBs: Essential for relationship-heavy data. Neo4j and Amazon Neptune excel in scenarios like fraud detection or knowledge graphs. 4. Document DBs: MongoDB and Couchbase offer flexibility for rapidly changing schemas and horizontal scaling. Ideal for content management and real-time big data. 5. Columnar DBs: Vertica and ClickHouse provide superior performance for analytical workloads, especially in data warehousing scenarios. 6. In-Memory DBs: Redis and Memcached shine in caching layers and real-time bidding systems where sub-millisecond response times are critical. 7. NoSQL DBs: Beyond basic key-value stores, consider specialized options like Cassandra for write-heavy, distributed systems. 8. Relational DBs: Postgres and MySQL continue to evolve, now offering JSON support and improved scalability. Still the go-to for transactional systems. 9. Vector DBs: Milvus and Pinecone are pushing boundaries in AI applications, offering efficient similarity search for large-scale machine learning models. Key considerations: - Data model complexity - Read/write patterns - Scalability requirements - Consistency vs. availability tradeoffs - Operational costs and expertise Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

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This is an interesting bug I along with Ankur Jaiswal encountered while working to fetch Custom Metadata records using flow with multiple complex conditions. We had to identify the records based on range fields(Min value and Max Value) and when we started to debug the flow, we got the error - "Disjunctions not Supported". This was an error which I had never encountered before and we started freaking out to understand the issue. That is when this blog here written by Bob Hatcher - https://lnkd.in/giYMmMYn helped us to understand the issue. After some research it became clear that, this is the error we get when you try to query a custom metadata table using an "OR" condition. But the Flow didn't use an "OR" condition in the metadata lookup. Now the solutions that we had to do was to migrate entire data to Custom Object or do apex callout to get the records using SOQL which was not feasible for our "Low Code-No Code" approach. Thanks to this anonymous guy who came up with a solution within the same blog and made wonders to us. This is a great blog to read and solution is described in the comment section of the blog. Please read through. #Salesforce #Flow #CustomMetadata

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https://lnkd.in/gtzi8qds Came cross this amazing text prompt to Diagram tool. Really enterprise level diagrams #10x #aiispresent

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🧠 𝗙𝗠𝗢𝗽𝘀 𝘃𝘀. 𝗠𝗟𝗢𝗽𝘀: 𝗛𝗼𝘄 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗔𝗜 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 We’re witnessing a significant transformation in how machine learning models are developed and deployed. With the rise of 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗙𝗠), 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗠𝗟𝗢𝗽𝘀 𝘁𝗼 𝗙𝗠𝗢𝗽𝘀 is bringing efficiency and scalability to the forefront of AI development. 🌐 🔍 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? 1. 𝗠𝗟𝗢𝗽𝘀 (𝗖𝗼𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗟 𝗙𝗹𝗼𝘄): 🔹 Involves task-specific model training, hyper-parameter tuning, and validation for each project. 🔹Requires iteration and fine-tuning for every specific use case. 🔹𝗘𝗮𝗰𝗵 𝗻𝗲𝘄 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 = 𝗻𝗲𝘄 𝗺𝗼𝗱𝗲𝗹 🛠️ 2. 𝗙𝗠𝗢𝗽𝘀 (𝗠𝗟 𝗙𝗹𝗼𝘄 𝘄𝗶𝘁𝗵 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀): 🔹 Uses a foundation model that is trained or tuned once and then adapted across multiple tasks. 🔹 Instead of retraining models from scratch, FMs allow for fine-tuning for each use case, leading to faster deployments. 🔹 𝗢𝗻𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹, 𝗺𝗮𝗻𝘆 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀! 🌟 💡 𝗞𝗲𝘆 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲? 𝗕𝘆 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝘁𝗼 𝗙𝗠𝗢𝗽𝘀, 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻 𝘀𝗰𝗮𝗹𝗲 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 𝗯𝘆 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 𝗮𝗰𝗿𝗼𝘀𝘀 𝘃𝗮𝗿𝗶𝗼𝘂𝘀 𝘁𝗮𝘀𝗸𝘀. As businesses look to accelerate their 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗲𝗳𝗳𝗼𝗿𝘁𝘀, 𝗮𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗙𝗠𝗢𝗽𝘀 𝗰𝗼𝘂𝗹𝗱 𝗽𝗿𝗼𝘃𝗶𝗱𝗲 𝗮 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲. Courtesy: Mitra AI DeepLearning.AI Flower Labs Pragmatic AI Labs Mitra Robot #AI #MLOps #FMOps #FoundationModels #MachineLearning #DigitalTransformation #AIDeployment #EnterpriseAI #DataScience #Innovation #TechnologyLeadership #Automation #FutureOfWork

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🌟 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐭𝐡𝐞 𝐑𝐀𝐆 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐟𝐨𝐫 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 🚀 📘 In the digital age, managing and retrieving information from vast datasets can be daunting. The RAG (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) Framework is revolutionizing how we handle this by integrating machine learning models with vector database retrieval to provide precise answers to complex queries. 🔍 𝐇𝐞𝐫𝐞'𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐋𝐨𝐚𝐝𝐞𝐫: Text documents, such as PDFs, are loaded into the system. 𝐓𝐞𝐱𝐭 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: The raw text is cleaned and formatted to ensure high-quality data input. 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: The pre-processed text is transformed into numerical embeddings that capture the essence of the data. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: These embeddings are stored in a vector database that facilitates fast and accurate retrieval. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐐𝐀 𝐂𝐡𝐚𝐢𝐧: Upon querying, the system retrieves the most relevant information, ensuring that the response is both accurate and contextually appropriate. 𝐑𝐞-𝐏𝐡𝐫𝐚𝐬𝐢𝐧𝐠 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦: It refines both the input query and the output, enhancing clarity and relevance. 👁️‍🗨️ This methodology not only streamlines the retrieval process but also enhances the accuracy and efficiency of information extraction, making it a cornerstone for developers and researchers dealing with large datasets. 💡 Interested in learning more about how RAG can be implemented in your projects? Let’s connect and explore how this framework can transform your data management strategies! #stayhungry #keeplearning #DataScience #MachineLearning #InformationRetrieval #TechInnovation #RAGFramework #AI #BigData #generativeai #innovation #technology #vectordatabase #artificalintelligence #smartfactory

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The Performance Schema in #MySQL is a robust tool for monitoring and diagnosing server activity, including Input/Output (I/O) operations. It provides detailed insights into I/O bottlenecks by collecting and organizing performance-related data, enabling database administrators to identify issues and optimize performance effectively. #MySQLPerformance @MinervaDB #dba https://zurl.co/yF0E

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Personalized Programs - Iteration 1 When building personalized programs using machine learning algorithms like Random Forest and K-Means, feature scaling is a crucial preprocessing step. The choice of feature scaling method can significantly influence model performance. Although StandardScaler is commonly used for various machine learning algorithms, it assumes that the features follow a normal distribution. In our initial approach, we used StandardScaler with a plan to handle outliers during the data preprocessing stages. However, the features did not follow a normal distribution and were light-tailed, with a kurtosis value of less than 3 and were right-skewed in nature. So, the alternative was to use advanced scalers and we opted for RobustScaler to maintain the integrity of the feature ranges. The added complexity to our pipeline was justified as we observed improved results. We then incorporated the RandomizedSearchCV technique to find the optimal hyperparameters for our Random Forest model, which further boosted performance. That's the end of iteration 1. What techniques do you follow to increase the accuracy of an ML model? #machinelearningengineering #mlops #datapreprocessing #modelperformance #modeloptimization #featurescaling

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🏆 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲: 𝗔𝗹𝗶𝗴𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗧𝘆𝗽𝗲 𝘄𝗶𝘁𝗵 𝗪𝗼𝗿𝗸 𝗢𝗿𝗱𝗲𝗿 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗼𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 🌟 🏭 In manufacturing, 𝗼𝗻𝗲 𝘀𝗶𝘇𝗲 𝗱𝗼𝗲𝘀 𝗡𝗢𝗧 𝗳𝗶𝘁 𝗮𝗹𝗹! Each industry—from 𝗔𝗲𝗿𝗼𝘀𝗽𝗮𝗰𝗲 𝗮𝗻𝗱 𝗔𝘂𝘁𝗼𝗺𝗼𝘁𝗶𝘃𝗲 𝘁𝗼 𝗟𝗶𝗳𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗚𝗼𝗼𝗱𝘀—demands a unique approach to both production and data management. 💡 𝗠𝘆 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗮 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗯𝗹𝗲𝗻𝗱 𝗼𝗳 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗧𝘆𝗽𝗲𝘀 (Discrete, Batch/Hybrid, Continuous) and Work Order Types (Engineer-to-Order, Make-to-Order, Make-to-Stock) across industries. These combinations drive data ingestion and infrastructure needs, creating a dynamic ecosystem that adapts to volume, speed, and compliance. 💬 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵:  1️⃣ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗧𝘆𝗽𝗲𝘀 𝗠𝗮𝘁𝘁𝗲𝗿: Each industry—whether 𝗔𝗲𝗿𝗼𝘀𝗽𝗮𝗰𝗲, 𝗔𝘂𝘁𝗼𝗺𝗼𝘁𝗶𝘃𝗲, 𝗟𝗶𝗳𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀, 𝗼𝗿 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗚𝗼𝗼𝗱𝘀—operates with specific 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗧𝘆𝗽𝗲𝘀 (Discrete, Batch/Hybrid, Continuous). 2️⃣ 𝗔𝗹𝗶𝗴𝗻𝗶𝗻𝗴 𝗪𝗼𝗿𝗸 𝗢𝗿𝗱𝗲𝗿 𝗧𝘆𝗽𝗲𝘀: Three primary work order types—𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿-𝘁𝗼-𝗢𝗿𝗱𝗲𝗿 (𝗘𝗧𝗢), 𝗠𝗮𝗸𝗲-𝘁𝗼-𝗢𝗿𝗱𝗲𝗿 (𝗠𝗧𝗢), 𝗮𝗻𝗱 𝗠𝗮𝗸𝗲-𝘁𝗼-𝗦𝘁𝗼𝗰𝗸 (𝗠𝗧𝗦)—are used in combination across industries. Understanding which combinations work best helps tailor operations to meet market demands efficiently. 3️⃣ 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝘀 𝗮 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗼𝗿: Each of the 9 possible combinations of production and work order types has distinct data ingestion and application infrastructure needs. 4️⃣ 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 3 𝗩’𝘀—𝗩𝗼𝗹𝘂𝗺𝗲, 𝗩𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗩𝗮𝗹𝘂𝗲: To thrive, manufacturing ecosystems must adapt swiftly to changes in volume, address variability in customer demand, and continuously add value across the supply chain. 🚀 To the #𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆40 𝗮𝗻𝗱 #𝗦𝗺𝗮𝗿𝘁𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: How are you seeing these production and work order complexities play out in your field? Let’s share insights, learn from each other. #ExecutiveInsights #ManufacturingLeadership #ProductionOptimization #DigitalTransformation #SmartManufacturing #ManufacturingExcellence #Industry40 #ManufacturingInnovation #OperationalExcellence #DataDrivenDecisions #ManufacturingStrategy #IndustrialAutomation #BusinessTransformation #InnovationLeadership #Adaptability #FutureOfManufacturing #mes

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