This detailed guide showcases advanced technical expertise in building and deploying production-ready AI solutions using AWS Services like Bedrock. Perfect for developers with 2+ years of cloud experience looking to advance their careers. For organizations investing in AI initiatives, this post provides a reliable way to identify and verify developers who can move beyond proofs-of-concept to build production-grade generative AI solutions that deliver tangible business results

If you are prepping for the AWS Certified AI Practitioner   https://aws.amazon.com/certification/certified-ai-practitioner/ ,  these notes should be enough to get the fundamentals for the exam. Domain 8: Essentials of Prompt Engineering Understand Prompts : By interacting with a model through a series of questions, statements, or instructions, you can adjust model output behavior based on the specific context of the output that you want to achieve. Using effective prompt stra

If you are prepping for the AWS Certified AI Practitioner   https://aws.amazon.com/certification/certified-ai-practitioner/ ,  these notes should be enough to get the fundamentals for the exam. Domain 7: Security, Compliance, and Governance for AI Solutions Amazon Web Services (AWS) provides a comprehensive set of tools, services, and partner solutions to build and secure artificial intelligence (AI) systems. These resources help achieve compliance objectives, such as protect

If you are prepping for the AWS Certified AI Practitioner   https://aws.amazon.com/certification/certified-ai-practitioner/ ,  these notes should be enough to get the fundamentals for the exam. Domain 6: Optimizing Foundation Models Explore two techniques to improve the performance of a foundation model (FM): Retrieval Augmented Generation (RAG) - Provide specific knowledge dataset for the tuning of the model. Fine-Tuning - Although foundation models are highly versatile, t

If you are prepping for the AWS Certified AI Practitioner   https://aws.amazon.com/certification/certified-ai-practitioner/ ,  these notes should be enough to get the fundamentals for the exam. Domain 4: Developing Machine Learning Solutions Machine learning (ML) lifecycle refers to the end-to-end process of developing, deploying, and maintaining machine learning models. With SageMaker Jumpstart , you can deploy, fine-tune, and evaluate pre-trained models from the most popula