Running Jupyter in Kubernetes with an SLA

jupyter_logo

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So Jupyter is a great tool for experimental science.  Running a jupyter notebook though can be tricky; especially if you want to maintain all of the data that is stored in it.  I have seen many strategies; but I have come up with one that I like best of all.  It is based on my “Micro Services for Data Science” strategy.  By using decoupled data and compute we can literally thrash our Jupyter notebook and all of our data and notebooks still live.  So why not put it in a self healing orchestrater and deploy via Kubernetes :D.

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Data Analytics Architectural Blueprint

Here is a video show casing a sample architecture for doing Data Analytics on Azure.  Enjoy 🙂

Adopting F#

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You can see from my blog that I have been increasing my adoption of F# and my love of the language is growing.  I find the language very conducive to cloud computing.  F# at its core seems to be built for distributed computing.  If you program with F# in an idiomatic way, you are setting yourself up for success on the cloud, as the cloud is a variably sized distributed system that you are deploying production code to.  That is what F# excels at.

As much as I have loved F#, it has not been all roses and sunshine.  There have been issues.  This article at its core is really an ask from the community to support me in building the case and evangelizing F# in such a way it can be recognized and adopted not only by the community, but by those who make the decisions to expend resources on additional tooling and support.  I must iterate that the contents of this article are in no way the opinion of Microsoft, but merely my own observations.  From these observations I have developed a strategy and action items that we the F# community must do to achieve these goals.

First, we must step outside of our normal mindset as F# developers and begin looking at the language from the outside perspective.  We must also look at some trends.

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Understanding AzureML – Part 2: Binary Classification

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Welcome to Part 2!  We will be discussing Binary Classification.  So I hope many of you have started using AzureML.  If not, you should definitely check it out.  Here is the link to the dev center for it.  This article series will focus on a few key points.

  • Understanding the Evaluation of each Model Type.
  • Understanding the published Web Service of each Model

If you are looking for how to build a simple how to get started, check out this article.

The series will be broken down into a three parts.

Part 1: Regression

Part 2: Binary Classification

 

So lets get started!

AzureML

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Understanding AzureML – Part 1: Regression

Hello World!

So I hope many of you have started using AzureML.  If not, you should definitely check it out.  Here is the link to the dev center for it.  This article will focus on a few key points.

  • Understanding the Evaluation of each Model Type.
  • Understanding the published Web Service of each Model

If you are looking for how to build a simple how to get started, check out this article.

The series will be broken down into a three parts.

Part 1: Regression

Part 2: Binary Classification

So lets get started!

AzureML

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