Past Meetup

RecDoTech - Data Science, Machine Learning, and Kubernetes

This Meetup is past

13 people went

Anchormen

Pedro de Medinalaan 11 · Amsterdam

How to find us

We're 14 mins from Central Station. Plus, there are two onsite car parks available, free of charge, for all attendee's.

Location image of event venue

Details

In order to secure your spot, reserve your ticket here: https://www.eventbrite.co.uk/e/recdotech-data-science-machine-learning-and-kubernetes-tickets-55014999344

RecDoTech is back, and we are meeting up again on 31st January at Anchormen's office in Steigerland, Amsterdam.

This meetup explores real-world Data case studies that will help you understand or improve your current workflows. If you're interested in how Kuberenetes and CI/CD pipelines are utilised in business, come and join us!

We also have free pizza and refreshments, we want to make sure that everyone has something to eat - so if you have any special dietary requirements let us know when you register.

The venue is located approximately 15 mins by tram from Centraal Station & on-site parking available for all attendees. The event will be held on the ground floor, and the building is also fully accessible.

Speaker #1

Angel Sevilla, Head of Data Science, Anchormen.

Continuous Integration (CI) and Continuous Delivery (CD) of GPU-based applications on google cloud (GCP) using Gitlab and Kubernetes.

Are you a data scientist who only wants to focus on modelling and coding and not on setting up a GPU cluster? Then, this presentation might be interesting for you. We will develop together an automated pipeline using Gitlab and Kubernetes which can run code in GCP without worrying about drivers or other hassles. Just by pushing your code, it runs in a GPU!

Speaker #2

Pratyush Kumar Sinha, Principal Consultant, Infosys

Finding structure in unlabelled data and how to commercialise it.

TBA

Agenda

18:00 – Doors open, networking, food and refreshments
18:30 – Talks and Presentations
20:30 – Networking and access to speakers
21:30 – Doors close