Mastering AWS for MLOps: A Step-by-Step Guide to Power Up Your Machine Learning Pipeline

AWS offers a suite of powerful MLOps tools to streamline machine learning from model development to deployment and monitoring. Here’s your guide to learning AWS for MLOps from scratch, with examples and resources to help you build an end-to-end pipeline.

Part 1: Getting Started with AWS Basics

1. Set Up Your AWS Account

• Sign Up: Go to AWS Sign-Up and create an account.
• AWS Free Tier: Take advantage of AWS’s free services for 12 months, which covers essential services like S3, EC2, and Lambda at no cost.
• IAM Configuration: Use AWS Identity and Access Management (IAM) to create roles and set permissions, ensuring secure access.

1. Learn the AWS Console and CLI

• AWS Management Console: Get familiar with the UI for a graphical approach.
• AWS CLI: Set up the CLI and practice basic commands like aws s3 ls to view S3 buckets.
• Resources:
• AWS Console Training
• AWS CLI Guide

Part 2: Data Management for Machine Learning

1. Store Data Using S3

• Amazon S3 Basics: Create an S3 bucket, upload, and organize data. Practice secure storage by managing bucket policies.