2011, Big Data Analytics and Data Scientist

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2011

Big Data Analytics

Data Scientist

Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

APRIL 2, 2019

These massive storage pools of data are among the most non-traditional methods of data storage around and they came about as companies raced to embrace the trend of Big Data Analytics which was sweeping the world in the early 2010s. The Thrust for Data Lake Creation.

Data Lakes

Data Lakes Big Data Big Data Data Scientist

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning Blog

APRIL 19, 2023

ML models make predictions given a set of input data known as features , and data scientists easily spend more than 60% of their time designing and building these features. This example notebook demonstrates the pattern of using Feature Store as a central repository from which data scientists can extract training datasets.

ML ML Apache Kafka Data Scientist

Data Science Current

Here’s Why Automation For Data Lakes Could Be Important

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

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