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AWS Certified AI Practitioner Exam — Domain 1: ML Lifecycle & Generative AI Concepts Welcome to Video 3 of our AWS Certified AI Practitioner series! In this video, we explore the machine learning development lifecycle, dive into foundational generative AI concepts, and understand how foundation models evolve from data to deployment. This is a key part of Domain 1 of the certification exam. 📚 In this video, you will learn: ✅ ML pipeline stages: Data collection, preprocessing, training, evaluation, deployment, monitoring 🧪 Model sources: Pre-trained vs custom models, managed vs self-hosted APIs 🛠️ AWS services for ML lifecycle: SageMaker, Data Wrangler, Feature Store, Model Monitor 🔁 MLOps fundamentals: Experimentation, scalability, monitoring, re-training 📊 Evaluation metrics: Accuracy, AUC, F1 Score, ROI, cost/user 🧠 Generative AI concepts: Tokens, embeddings, prompt engineering, transformers, foundation models, multi-modal & diffusion models 🎯 Use cases: Chatbots, summarization, translation, code generation, image/audio/video generation, recommendation engines 🔄 Foundation model lifecycle: Data selection, pre-training, fine-tuning, deployment, feedback 💡 Key Takeaways: Build a strong understanding of how ML systems are developed and deployed, and how generative AI is transforming industries. 📺 Watch Next: Video 4 will explain the basic concepts of generative AI in more detail — including how these models work and how they’re applied across domains. 🔔 Subscribe to stay updated with the full guided series and ace your AWS Certified AI Practitioner exam! 👍 Like, Share, and Comment if this video helped you understand ML lifecycle and generative AI foundations! #AWS #AIPractitioner #MachineLearning #GenerativeAI #AWSCertification #FoundationModels #MLOps #SageMaker #CLFAI01 #CloudComputing
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