#BlueCloudMirror Game – highscores

I am a part of the team along with @Niklas and @Harald , who created and developed the fun Blue Cloud Mirror Game. 

game

This is my first blog  about this topic, and I will follow with future posts to detail the different parts of highscores (scores) and the technologies and components involved.

You can also take a look at other existing information:

The motivation for the highscores (scores)

If you play a game, you usually want to compare yourself with others. To do this, you simply have to save scoring information somewhere and provide access for the players who want to see the highscores, even if they do not play the game.

The high-level architecture overview on scores

The objective was to implement this on the cloud with state-of-the-art runtimes, services, security topics, to cover microservices, and to have an easy scalable cloud architecture.
I developed the scores part and the functions-api for users.

scores-architecture

The Game, Scores Service UI and the Highscores webapp are hosted on different runtimes and for the execution they will be loaded into a browser.

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Cloud Foundry on top of Kubernetes @IBM Cloud – a small test

This blog is related to the topics Cloud Foundry Enterprise Environment , serverless , code patterns and the IBM Coder Program.

I just want to move an existing Cloud Foundry app on IBM Cloud to the new IBM Cloud Foundry Enterprise Environment,which instantiates Cloud Foundry on top of Kubernetes. You can find more details about this IBM Cloud offering in this blog post “An on-demand, single-tenant Platform-as-a-Service on IBM Cloudfrom IBM.

Motivation

Maybe you noticed the section Combined Power in my blog post Operations and Developers side by side @“Continuous Lifecycle” and “Container Conf” related to Simon Moser’s session CF3 – Combining the Power of Cloud Foundry and Kubernetes” ? In this blog post you can read more about the motivation to combine the power.  Also you can find out which open source projects ( eirini for example) are relevant.

Based on  Simon Moser´s speech, I was motivated to check out the Cloud Foundry Enterprise Environment on IBM Cloud.

I started my test of moving an existing Could Foundry App. I have chosen to move the app instance from one of my modified code patterns . My selection was the Predictive Industrial Visual Analysis. This code pattern has a big focus on serverless, but I will write more about the serverless part in the near future.

Let’s get started: How do I move an existing Cloud Foundry app?

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Let‘s Code Pattern 02: Diversity and Inclusion – ensure loan fairness

The good combination of IBM coder program  and code patterns motivated me to dig in into the data science area a little bit, relating to the real-world topic diversity and inclusion . Also it was interesting to see: How the AI Fairness 360 toolkit, which is an open-source library to help detect and remove bias in machine learning models, can be used for data science in this context.

The diversity and inclusion topic started with a good statement:

Diversity and inclusion is more than a checkbox, it’s fuel for growth and success in any business or community.” IBM Coder Program.

This was one reason why I completed the challenges of the Diversity and Inclusion topic in the IBM coder program .

The IBM coder program

If you don’t know the IBM coder program, just visit this  link .
The program is about:

  • Growing skillset and reputation
  • Discovering and transparently discuss content 
  • Being rewarded with various tangible and intangible rewards

The code pattern Ensure loan fairness  was one of the challenges for Diversity and Inclusion topic.

Code pattern “Ensure loan fairness”

This code pattern is about machine learning with artificial intelligence, data science and python. It gives an introduction in how to use the AIF360 tool kit .

  • “The AI Fairness 360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models.“

In the related demo on IBM research-trusted UI, you can verify given ai models with different datasets and the bias mitigation algorithms .

BLOG-pattern-02-image

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