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AWS Certified AI Practitioner Exam — Domain 3: Applications of Foundation Models Welcome to Video 9 of our AWS Certified AI Practitioner series! In this video, we dive deep into the training and fine-tuning process for foundation models. You’ll learn how these large-scale models are built, adapted for specific domains, and aligned with human feedback to deliver safe and effective outputs. 📚 In this video, you will learn: ✅ Key elements of training: Pre-training, Fine-tuning, Continuous Pre-training 🔍 Fine-tuning methods: Instruction Tuning, Domain Adaptation, Transfer Learning 📊 Data preparation essentials: Data curation, Governance, Size, Labeling, Representativeness 🤖 RLHF: Reinforcement Learning from Human Feedback for safer AI outputs 💡 Key Takeaways: Understand how foundation models evolve from general-purpose systems to domain-specific solutions, and why data quality and human feedback are critical for success. 📺 Watch Next: Our next video will cover methods to evaluate foundation model performance — including metrics like ROUGE, BLEU, and BERTScore. 🔔 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 foundation model training and fine-tuning! #AWS #AIPractitioner #GenerativeAI #FoundationModels #MachineLearning #AWSCertification #RLHF #FineTuning #CloudComputing
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