Welcome to the sixth episode of Analytics Bytes: a video series where we explore common startups analytics use cases and how to architect for them. Garbage-in is Garbage-out. The incomplete and inaccurate data, or the data that reflects underlying human prejudices leads to inaccurate predictions. The quality of the data goes a long way toward determining the quality of the Machine Learning result and their accuracy. In this episode, learn how to use Amazon SageMaker features such as Data Wrangler, GroundTruth, FeatureStore, Studio, and Studio Notebooks to do data preparation, feature engineering, and set up datasets for ML training. Key take aways of this sesison are – 1/why do you need separate data prep for ML, 2/what are different sagemaker features you can use for data prep, and 3/customers success stories.
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