Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines Build and deploy end-to-end AI/ML pipelines: Streamline data ingestion, preprocessing, model training, evaluation, and deployment. Automate pipeline execution: Schedule and monitor pipelines to ensure continuous data processing and model updates. Leverage AWS cloud services: Utilize AWS services like S3, SageMaker, and Lambda to optimize pipeline performance and scalability. Implement best practices: Adhere to industry standards for data governance, security, and model monitoring. * Gain practical experience: Hands-on exercises and real-world examples to enhance your understanding.