All videos
Opera view

Life after the model

April 16, 2018

Convolutional neural network is ready, F1 score calculated and ROC curve drawn. So you have a model. This tale is about what happens next: when and how to cleanse the model, how do push it into production and what defines quality for the model. I will show it using one of the projects we deployed here at AirHelp. I will also try to show a few tips & tricks that helps me manage Machine Learning projects.

Tags

Data

About the speaker

Michał Jakóbczyk

Michał Jakóbczyk

Software Engineering Director at Airhelp
Primarily I'm a Software Engineer. For last fifteen years doing Big Data, data analysis, machine learning, product and project management. For last several years focused…
Read more

Other videos that you might like

fallback image
Data
Programmer 2.0
Vladimir Alekseichenko
fallback image
Data
Explaining neural networks predictions
Matthew Opala
fallback image
Data
pandas-stubs — How we enhanced pandas with type annotations
Joanna Sendorek, Zbyszek Królikowski
fallback image
Data
Stream processing in telco – case study based on Apache Flink & TouK Nussknacker
Maciek Próchniak
All videos