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Principal Architect | Technology Adoption | Partnerships | Manufacturing IT | Tech Enthusiast I Six Sigma Black Belt | MES | MOM | GPTIS | Factory Security | Innovator | AI | ML | LLM | *Ops | Lifelong Learner

🧠 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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