Noisy labels can wreak havoc

Part 3 in the Fraud ML series

Challenges of ML with PII data

Part 2 in the Fraud ML series

How to build better fraud detection systems with ML

Part 1 of the Fraud ML series

Signal : Noise

Dealing with variation in experimental results

Machine Learning Security

Exploring the vulnerabilities of ML models from attack by bad actors. They're more fragile than you might think

Automated Hyper-parameter Optimization in SageMaker

A tutorial in Python

Implementing Git in Data Science

Some thoughts on how to apply version control when conducting experiments

Committing to Sanity

Why git is critical for Data Science

More than Algorithms

Some important considerations for building and deploying models in industry

Sharing your SageMaker model

How to give access to your SageMaker models to another AWS account