Bridging the Gap: from Data Science to Production

A recent but quite common observation in industry is that although there is an overall high adoption of data science, many companies struggle to get it into production. Huge teams of well-payed data scientists often present one fancy model after the other to their managers but their proof of concepts …

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Managing isolated Environments with PySpark

The Spark data processing platform becomes more and more important for data scientists using Python. PySpark - the official Python API for Spark - makes it easy to get started but managing applications and their dependencies in isolated environments is no easy task.

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Efficient UD(A)Fs with PySpark

Nowadays, Spark surely is one of the most prevalent technologies in the fields of data science and big data. Luckily, even though it is developed in Scala and runs in the Java Virtual Machine (JVM), it comes with Python bindings also known as PySpark, whose API was heavily influenced by …

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