Name: Why should you consider Kubeflow if you are thinking machine learning?
Start: 2019-10-30T18:00:00+02:00
End: 2019-10-30T20:00:00+02:00
Location: Google, Johannesburg

Cloud Native Computing Johannesburg has partnered with GDG Cloud Johannesburg to bring you yet another exciting meetup!

Join us at our next event at the Google Offices in JHB where we will cover all things Kubflow, we will outline how Kubeflow addresses some of the pain points in a data scientist’s life. We will focus on what Kubeflow is, how it can be incorporated into your existing workflow and how Kubeflow enables collaboration within your data team.
We will also demo a simple Kubeflow pipeline that can be used in a typical data science workflow.

Free pizza and drinks will be available

Please see below some information om our speakers and the topics they will cover.

Aneesh Chandran:
I am a software engineer working at standard bank. I am an AI enthusiast and have a passion for Robotics and AI. I am currently aspiring to be a machine learning engineer and am currently involved with the Deep Learning Indaba X community in south africa.

Topic: Why should you consider Kubeflow if you are thinking machine learning?
Kubeflow is built by a community of data scientists and data engineers to address the pain points of productising machine learning solutions.
In this talk, we will outline how Kubeflow addresses some of the pain points in a data scientist’s life. We will focus on what Kubeflow is, how it can be incorporated into your existing workflow and how Kubeflow enables collaboration within your data team.
We will also demo a simple Kubeflow pipeline that can be used in a typical data science workflow. Which will hopefully inspire developers to further use Kubeflow or at least get the organisation to consider Kubeflow in their architecture. A brief overview of the different open source software used within Kubeflow will also be discussed.
This talk is geared towards data scientists, machine learning engineers, data engineers and anyone who is a data science enthusiast.

Harry Lee:
Is a DevOps engineer and evangelist with a strong background in the
financial technology sector. His mission is to continuously deliver
business value by ensuring high availability and scalability of the
business services. His expertise is in designing and implementing
cloud-native solutions, propagating the DevOps culture and providing DevOps training across the organisation.

Topic: Build with the end in mind: infrastructure-backed data science with Kubeflow
As data scientists, we usually prototype use cases and try to find the one that can generate business value with the data on hand. We jump straight to work and at the end of the PoC accidentally wow-ed the stakeholders so much that they want the solution in production tomorrow.
We scramble around our Jupyter notebooks and scripts to put together a pipeline that we think is reliable, the infrastructure guy then turns
around and says "I can't use any of this".
At Melio, we develop with deployment in mind with Kubeflow. From the
beginning, infrastructure sits with data science to gather the requirements for production. We set up the Kubeflow pipeline to allow our experiments to run exactly as how it will be run in production. From the data scientist's perspective, it's the same as writing notebooks; from the infrastructure, it's the same as setting up Kubernetes.
In this talk, we will be presenting our data science workflow with Kubeflow both from the operation's and data scientist's standpoints. We
will also demonstrate how we have incorporated Kubeflow into our profile image analyser pipeline.

Harry Lee

Cloud Native Computing Johannesburg

Technology

Open Source

Cloud Computing

Prometheus

DevOps

Docker

Kubernetes

Microservices

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Why should you consider Kubeflow if you are thinking machine learning?

Google, Johannesburg

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Why should you consider Kubeflow if you are thinking machine learning?

Cloud Native Computing Johannesburg

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