Serverless Data Science on AWS: Deploying Scalable Pipelines

Traditional infrastructure management can slow down data science projects with unnecessary server maintenance and scaling headaches. Transitioning to serverless architecture allows you to focus purely on data analysis and model deployment while the cloud handles the underlying infrastructure. This text-only course guides you through the core concepts of serverless computing tailored specifically for data workflows, helping you transform raw scripts into automated, cost-efficient pipelines. What you'll learn: - Understand foundational serverless concepts and cloud infrastructure terminology. - Deploy data-processing code efficiently using AWS Lambda functions. - Orchestrate multi-step data workflows and pipelines using AWS Step Functions. - Process large datasets using AWS SDK for pandas and modern dataframe techniques. - Configure basic monitoring, logging, and observability for serverless applications. - Apply cost-optimization strategies to keep your data pipelines highly efficient. You will start by mastering key cloud concepts and basic definitions before diving into structured written guides. Through clear text explanations and practical code snippets, you will learn to build, connect, and monitor robust serverless data solutions step-by-step. This course is designed for data scientists, analysts, and developers who are new to cloud architecture, requiring no prior serverless experience. Start reading today to build scalable, low-maintenance data pipelines in the cloud.