Name: Deploying Machine Learning Pipelines in Production: Overcoming common pitfalls
Start: 2017-01-19T18:00:00-05:00
End: 2017-01-19T20:00:00-05:00
Location: Breather HQ

Building a machine learning model that can predict well is one challenge, productionizing it is quite another challenge. How to ensure stability and speed over time and as your data grows in size? How to measure and prevent drift? Come hear about the experiences of machine learning engineers at Google, Stripe, and Shopify.

### Speakers ###

* Julia Evans, Software Engineer, Stripe
* Philippe Hamel, Machine Learning Engineer, Google
* Peng Yu, Merchant Optimization Developer, Shopify

### Schedule ###

6:00 | Doors open

6:30 | Presentations

Co-hosted by Breather, Shopify, and MTL DATA

Matthieu Di Mercurio

Vaughn DiMarco

Françoise

MTL Data

Technology

Open Source

Business Intelligence

Big Data

Machine Learning

Data Analytics

Data Visualization

Predictive Analytics

Data Mining

Open Data

Business Analytics

Data Science

Applied Statistics

Deploying Machine Learning Pipelines in Production: Overcoming common pitfalls

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Deploying Machine Learning Pipelines in Production: Overcoming common pitfalls

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