When you first explore machine learning on AWS, you will definitely feel overwhelmed by the number of services available. But once you focus on the core tools, it became much clearer which ones actually matter for building real projects.
Here are 7 essential tools you should know:
1. Amazon SageMaker
This is the main ML service on AWS. It helps you build, train, and deploy models quickly without managing infrastructure.
2. AWS Lambda
Useful for running ML code or triggering tasks automatically without managing servers.
3. Amazon S3
Stores datasets, models, and outputs securely. It acts as the backbone for data in ML workflows.
4. Amazon EC2
Provides computing power for training large models when you need full control over resources.
5. AWS Glue
Helps clean, transform, and prepare data before feeding it into ML models.
6. Amazon Redshift
Used for analyzing large datasets and running complex queries efficiently.
7. Amazon CloudWatch
Tracks performance, logs, and system health to ensure your ML systems run smoothly.
These tools together cover the full ML pipeline from data storage and processing to model training and deployment, which makes AWS a powerful platform for machine learning projects.