Prompt engineering for AWS has transformed the way I work in cloud consulting. By learning to give AI tools like Amazon Q and Amazon Bedrock the right context, constraints, and roles, I’ve gone from getting generic answers to receiving tailored, cost-saving solutions. In this post, I share the practical techniques that turn AI into a true partner for solving complex AWS challenges.
Agentic AI is full of promise but also full of hype. In this blog, I cut through the noise to explore how enterprises can pragmatically adopt Agentic AI, starting with strong data foundations, human-in-the-loop workflows, and a responsible approach to governance. From the “Iron Man Suit” philosophy to avoiding shadow AI, this is a roadmap to real, sustainable impact, not science fiction.
Explore how AI and Generative AI are transforming DevSecOps practices and workflows.
Explore when Generative AI isn’t the best tool and why traditional machine learning models often provide better solutions in specific scenarios.
Explore how to build a secure, AI-powered code analysis application with AWS Bedrock and CloudFormation, ensuring compliance and efficiency for enterprise environments.
Explore how Amazon GuardDuty’s extended threat detection capabilities help protect AWS environments from sophisticated multi-stage attacks using AI and ML-driven insights.
Discover how Amazon SageMaker Pipelines can streamline MLOps for improved malaria diagnosis and overall healthcare efficiency.
Discover the ethical challenges and responsibilities of AI as it increasingly influences our daily lives and industries.
Discover how Amazon SageMaker with MLflow simplifies machine learning experimentation, making it easier for practitioners to manage their workflows.
Discover how AI encompasses more than just Generative AI, delving into its rich history and the importance of foundational techniques.
