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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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Top Data science books you should definitely read

Apiumhub

Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more. Top Data science books you should definitely read.

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Predictive analytics helps Fresenius anticipate dialysis complications

CIO

In September 2021, Fresenius set out to use machine learning and cloud computing to develop a model that could predict IDH 15 to 75 minutes in advance, enabling personalized care of patients with proactive intervention at the point of care. CIO 100, Digital Transformation, Healthcare Industry, Predictive Analytics

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The Good and the Bad of Hadoop Big Data Framework

Altexsoft

The biggest star of the Big Data world, Hadoop was named after a yellow stuffed elephant that belonged to the 2-year son of computer scientist Doug Cutting. Source: The Wall Street Journal. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics.

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Topics to watch at the Strata Data Conference in New York 2019

O'Reilly Media - Ideas

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.