Auger.AI Events

Below are events which include conferences and webinars where the Auger team will be presenting/attending.


2019 Past Events

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Webinar 1/16/2019 12:00 pst

Best Practices in Machine Learning Prediction With Auger

  1. Join us for a session where we discuss how to build optimal machine learning-based prediction models, with the Auger.AI Automated Machine Learning Service. We will discuss several topics:
    1. sizing your instances based on data size
    2. choosing algorithms (i.e. restricting search) based on data dimensionality (rows and columns) and data distribution (edited)
    3. restricting search based on explainability
    4. restricting search based on prediction runtime deployment size and speed
    5. choosing the appropriate accuracy metric to optimize for

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Webinar Mar 6, 2019 12:00PM

Integrating Auger.AI Into Your Data Science Pipeline and Applications

In this webinar, we will show how to assemble a training dataset CSV dynamically from disparate sources, execute experiments, promote a winning pipeline and produce predictions from the promoted pipeline using new data.  

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Global big data conference Jan 23 -25, 2019

Come visit us in Booth 4 at the Santa Clara Convention Center from Jan 23 - 25th at the 3rd Annual Global Big Data Conference. Come by and get a demonstration and talk with our engineers and data scientists about your particular needs.

Webinar 2/6/2019 8:00pm pst

Best Practices in Machine Learning Prediction With Auger

  1. Join us for a session where we discuss how to build optimal machine learning-based prediction models, with the Auger.AI Automated Machine Learning Service. We will discuss several topics:
    1. sizing your instances based on data size
    2. choosing algorithms (i.e. restricting search) based on data dimensionality (rows and columns) and data distribution (edited)
    3. restricting search based on explainability
    4. restricting search based on prediction runtime deployment size and speed
    5. choosing the appropriate accuracy metric to optimize for