Automate Machine Learning pipeline using MLBox

The power of machine learning comes with a price. Once you have the skills, the toolkit, the hardware, and the data, there is still the complexity involved in creating and fine-tuning a machine learning model.

But if the whole point of machine learning is to automate tasks that previously required a human being, wouldn’t it be possible to automate this machine learning pipeline itself?

Automated Machine Learning has become a topic of considerable interest over the past year. It has been generating notable interest and gaining respect.

 

 

Structure of the hack-session

This is an 1-hour Hack-Session and includes the following modules:

 

  • The challenges that companies face today while finding the best ML solution
  • How automated machine learning is solving the above challenge?
  • Overview on the existing solution: MLBox as a full automated tool & open source package
  • A walkthrough of using MLBox to solve machine learning problems

 

Make sure you don’t miss this hack-session on automating machine learning pipeline! Get your tickets today to access this session.

 

INSTRUCTOR

AXEL DE ROMBLAY

 

Axel Aronio De Romblay is a Data Scientist at Groupe Crédit Agricole, Paris. He is passionate about Deep Learning and AutoML (Automated Machine Learning). He created MLBox. He has done his master’s in Data Science at “Ecole Polytechnique” (top French engineering school)

 

He has worked on various domains : machine learning, automated machine learning, NLP/chatbot, deep learning, time series, and worked on Languages : Python, Spark, Java, many kore.

 

Previously, he was a Data Scientist in the R&D DataLab at BNP Paribas Cardif (worlwide insurance company)where he worked on Fraud detection. He has also worked in the DataLab at Ubisoft (French video game publisher).

Most importantly, Axel is the Author of “MLBox” automated machine learning python package. He is a Kaggle Expert and passionate about Machine Learning and Mathematics

Duration of Hack-Session: 1 hour

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