Name: ML in practice - interaction between ML modeling and downstream design decisions
Start: 2022-12-06T17:30:00+01:00
End: 2022-12-06T20:30:00+01:00
Location: Ericsson AB

The theme of this talk is centered around Machine Learning from an engineering perspective. Here, accuracy is often the least interesting aspect of model selection, and very few real-world use cases depend on some 0.01% improved accuracy on ImageNet. This talk will demonstrate a particular project at Ericsson for predicting mobility in telecommunications networks. The use case will be used as a demonstrator in terms of the impact of model selection on the final task, and the sometimes very large implications a seemingly simple model choice will make on the final solution. If time (and audience) permits, the speaker will also go through a custom wait-free implementation of a sparse graph utilizing approximate updates implemented for this particular use case.

**Speaker**
Jesper Derehag, Sr. Machine Learning Engineer @ Ericsson AB

**Schedule**
17.00 - Doors open, and mingle
18.00 - Talk: Machine Learning in practice
19.30 - Food & mingle

**Important info**
You have to register at the Ericsson reception to be let in. The reception closes at 18, so you have to pass the gate before that time.

Niklas Antoncic

Jakob Andersson

Danila Petrelli

Erika Paul

Jesper Derehag

Gothenburg Artificial Intelligence Alliance (GAIA)

Technology

Artificial Intelligence

Big Data

Computer Science

Natural Language Processing

Machine Learning

Data Visualization

Predictive Analytics

Data Mining

Data Science

Data Analytics

Business Intelligence

Data Engineering

ML in practice - interaction between ML modeling and downstream design decisions

Ericsson AB

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Gothenburg Artificial Intelligence Alliance (GAIA)

ML in practice - interaction between ML modeling and downstream design decisions

Gothenburg Artificial Intelligence Alliance (GAIA)

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