JAX | 23. - 27. April 2018, Mainz

Decision Making based on Machine Learning at Outfittery

Session
Dieser Talk stammt aus dem Archiv. zum AKTUELLEN Programm
Nur bis 21. Dezember! ✓ Gratis Agile Day ✓ Smartwatch for free ✓ Save up to 956 € Jetzt anmelden
Infos
Dienstag, 7. November 2017
11:45 - 12:45
Raum:
Garmisch

Outfittery’s mission is to provide relevant fashion to men. In the past it was our stylists that put together the best outfits for our customers. But since about a year ago we started to rely more on intelligent algorithms to augment our human experts. This transition to become a data driven company has left its marks on our IT landscape: In the beginning we just did simple A/B tests. Then we wanted to use more complex logic so we added a generic data enrichment layer.Later we also provided easy configurability to steer processes.And this in turn enabled us to orchestrate our machine learning algorithms as self contained Docker containers within a Kubernetes cluster. All in all it’s a nice setup that we are pretty happy with.  

It then really took us some time to realise that we actually had built a delivery platform to deliver just any pure function that our data scientists come up with – directly into our microservices landscape. We just now started to use it that way; we just put their R&D experiments directly into production… 🙂 

This talk will guide you through this journey, explain how this platform is built, and what we do with it.

Alle News der Java-Welt:
Alle News der Java-Welt:

Behind the Tracks of JAX 2018

Agile & Culture
Teamwork & Methoden

Big Data & Machine Learning
Speicherung, Processing & mehr

Clouds, Container & Serverless
Alles rund um Cloud

Core Java & JVM Languages
Ausblicke & Best Practices

DevOps & Continuous Delivery
Deployment, Docker & mehr

Microservices
Strukturen & Frameworks

Web Development & JavaScript
JS & Webtechnologien

Performance & Security
Sichere Webanwendungen

Serverside & Enterprise Java
Spring, JDK & mehr

Digital Transformation & Innovation
Technologien & Vorgehensweisen

Software Architecture
Best Practices