The Conference for Machine Learning Innovation

Productionizing Machine Learning Models: Lessons learned in the Hadoop Ecosystem

Session
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✓ Workshop day for free
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Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
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✓10% Team Discount
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Register until November 7th:
✓Save up to € 210
✓10% Team Discount
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Infos
Tuesday, June 18 2019
11:30 - 12:30
Room:
Asam 1

The deployment of machine learning models can be challenging, especially in the context of distributed systems. Although Python is the dominant language among data scientists, it can create friction when integrating with JVM-based tools such as Spark or managing application dependencies on clusters of heterogeneous machines. Many data scientists developing on such systems struggle with the subtleties of these challenges. 

This presentation will share lessons learned working on large-scale Hadoop clusters and examine the most promising approaches to alleviate common issues. In particular, we will discuss our experience with leveraging containerization to tackle the dependency management challenge from a data scientist’s point of view.

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Online Edition Online Edition .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session Diese Session originates from the archive of stammt aus dem Archiv von MunichMunich . Take me to the current program of . Hier geht es zum aktuellen Programm von Online Edition Online Edition , Munich Munich , Singapore Singapore or oder Berlin Berlin .

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