AWS Compute for Data Science: EC2 vs. SageMaker vs. Lambda Explained

Описание: Welcome to our comprehensive guide on AWS Compute for Data Science. In this video, we break down the three most critical AWS services for machine learning and data processing: Amazon EC2, AWS SageMaker, and AWS Lambda.
Servers are the backbone of every data science task, but choosing the right one depends on your need for control versus flexibility. We compare these services across key factors including management overhead, cost models, scalability, and specific data science use cases.
What you will learn in this video:
✅ Amazon EC2: When you need full control and custom environments for high-performance computing.
✅ AWS SageMaker: Why it’s the best fit for end-to-end ML pipelines (Training, Tuning, and Deployment).
✅ AWS Lambda: How to leverage serverless, event-driven compute for lightweight ETL and real-time inference.
✅ Comparison Matrix: A side-by-side look at pricing, scalability, and maintenance.
✅ Scenario Testing: Real-world examples to help you decide which service to provision for your next project.
Whether you are a beginner looking to deploy your first model or an advanced practitioner optimizing for cost, this video will help you navigate the AWS management console with confidence.

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