Study notes: MLops Week 1-1 AWS Machine Learning Technologies

Table of Contents

Week 1-1 of the AWS MLops: Introduction

AWS Sagemaker Studio Lab
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Free environment for prototyping out machine learning projects
Based on Jupyter Notebook lab
Supports two compute types: CPU and GPU
Good integration with Hugging Face
Offers terminal window, allowing the access of AWS resources through the aws command

No credentials to manage because of the role-based privileges
Convenient file upload/download GUI
Easy access to AWS resources such as S3 via the aws command
- File transfer between CloudShell and S3 buckers
- File synchronization between CloudShell and S3 buckets
Cloud Developer Workspace
- Various venders:
  - Github CodeSpaces - Easy integration with Github services
  - AWS Cloud9, AWS CloudShell is a lightweight version of Cloud9
  - GCP Cloud IDE
  - Azure Cloud IDE
- Advantages comparing with traditional Laptop/Workstation
  - Powerful
  - Disposable
  - Preloaded
- Notebook-based: GPU + Jupyter Notebook
  - AWS Sagemaker Studio Lab
  - Google Colab Notebooks
AWS has pre-built machine learning applications that can be accessed directly in CloudShell
- Advanced text analytics
- Automated code reviews
- Chatbots
- Demand forecasting
- Document analysis
- Search
- Fraud prevention
- Image and video analysis
- …