Racheli is a Data Scientist at Riskified with 5 years experience in the Fraud Detection domain. She has led multiple projects regarding automating and optimizing ML solutions, and is currently working on data quality assessment processes.
Racheli is a visualizations enthusiast and loves the way a good chart can tell a story and make complex concepts clear to understand. Outside of her professional life, Racheli enjoys traveling, camping and Yoga.
When gathering data to train a ML model, the common belief is ‘the more the merrier’. In reality though, individual data samples may have varying effects on the learning process. How can we automatically measure the contribution of samples towards learning, and what can we do with it?