09b-sage-build.md

Sagemaker Build

Have a goal in mind before starting any machine learning task. Define your problem first

Data Preprocessing

Can take as long as you want, making it very important that we define our problem upfront. Primarily use SageMaker notebooks

• Visualise
• Explore
• Feature engineering: Can be done in Jupyter if its small, else EMR
• Synthesize data
• Convert data
• Change structure (join)
• Split data

Ground truth

"Build highly accurate training data using machine learning and reduce data labeling costs by upt to 70%"

Uses active learning to help label the most ambiguous data and outsources that to Mechanical Turk

SageMaker Algorithms

• AWS built-in algorithms
• AWS Marketplace: Crowd sourced, sometimes pre-built for transfer learning
• Custom: Make your own

Algorithm Types

• BlazingText: Word2vec (Amazon Comprehend)
• Image classification: CNN (Amazon Rekognition)
• K-means: Based off web-scale k-means clustering
• LDA
• PCA
• XGBoost