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Category: Technology

In this category I file blog posts about technology. If it is about the Internet of Things, agile development, a framework, a method, a pattern, or everything els technology relevant, I will post it here.

Review of the Predictive Analytics World Business Conference

Estrel Hotel Berlin
Event Location of Predictive Analytics World Business

The last two days I was at the Predictive Analytics World Business Conference in Berlin. The event happened inside the Estrel Hotel, a nice and good managed location. In the talks of day one, little was in for me. The deep dive tracks were too deep for me. The use case tracks too superficial. At least it looks like presenting companies are using AI/ML in production. This is in contrast to the Industrial Data Science Days in Dortmund earlier this year, where Companies are using AI/ML in scientific PoCs, far from production.

At day two, the talks were much more interesting. My personal highlight was the talk (with the very long title) “Data Science Development Lifecycle – Everyone Talks About It, Nobody Really Knows How to Do It and Everyone Thinks Everyone Else Is Doing It” by Christian Lindenlaub und René Traue. They summarized their learnings from using Scrum and other methods in Machine Learning projects. They showed how to combine different agile methodologies to run successful machine learning + production software projects. Very inspiring for our own projects too.

The following talk “How to Integrate Machine Learning into Serverless Workflows” delivered also some helpful insights for some of Tarent’s current projects.

In the end, a good conference with some points I took home. See you next year? I don’t know yet. We will see.

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Herausforderungen im Industrial IoT

Nachdem wir einige Industrial IoT (IIoT) Projekte umgesetzt haben, war es an der Zeit die Erfahrungen zusammenzutragen. Spannend sich da natürlich auch die Lösungen, die sich dazu bieten. Beides habe ich in einem Vortrag “Herausforderungen im Industrial IoT” zusammengetragen, den ich zuerst auf dem IoT Rhineland Meetup im Juni 2019 gehalten habe. Wir haben eine Aufzeichnung davon auf dem tarent YouTube Kanal veröffentlicht. (Die Produktion der Videos optimiere ich)

Die Herausforderungen im Industrial IoT:

  1. Viel Technik
  2. Vielfältigkeit der Schnittstellen und Ökosysteme
  3. Datenqualität
  4. Datenverarbeitung
  5. Plattform / Cloud – Ökosysteme

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Good Bye Deutsche Telekom – Hello Tarent Solutions

Exciting news: after 7+ years I will leave the Deutsche Telekom to join tarent solutions as Head of Emerging Technologies. All in all, my time at Deutsche Telekom was a journey of continuous learning. Besides all the quirks of a large enterprise, I was lucky to join great teams, do exciting projects and push products like the Cloud of Things. But time has come to CHANGE. Tarent solutions is a technology service provider with offices in Bonn, Cologne, Berlin and Bucharest. Developing innovative software solutions for more than 20 years, as well as integrating them into complex IT infrastructures. With the growing number and complexity of information technology and technology in general, it becomes more challenging to keep up with and focus on certain technologies. This is my new and continuing mission: “to explore strange new worlds, to seek out new life [tech] and new civilizations [communities], to boldly go where no one has gone…

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Do’s And Don’ts For Building A Developer Community

Support your developer community. Developers want to create successful stuff instead of try and error.
Support your developer community. Developers want to create successful stuff instead of try and error.

The great thing about the software tools today is, that most of them provide an API for developers. People can enhance, connect or create new products with the API. But how can you support developers and build a developer community or developer ecosystem? This Do’s and Dont’s list will help you to navigate through the unlimited possibilities of activities.

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6 Technology Trends for 2018

Every year, the innovation cycle gets shorter and shorter. Who know’s what technologies will emerge in 2018. But some big technology trends have recognizable growing attention. I predict that the following selected 6 technology trends will become sustainable in 2018.

Autonomous drones

Drones are still a cutting-edge technology, apart from multi-copters for filming purposes. Fully autonomous drones can be observed in closed areas, like logistic centers. Some prove of concepts already have been done in public for pizza delivery, postal service, or parcel delivery. In 2018, artificial intelligence and robotics will mature to commercial readiness.

Edge Computing

Multi-Access Edge Computing (or Mobile Edge Computing; MEC) will come commercially available in carrier networks. Even before the 5G standard will be finished, carriers will implement cloud computing capabilities in their networks. The question is, do they have the customer in mind and deploy a common system or do they create isolated solutions? Anyway, application builders can improve their customer experience with low latency or compute offloading solutions.

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Analyze Strava Training By Zones With Python

Example diagram - title image
Example diagram – title image

Effective training is key to build a strong foundation for competition. That’s why I am using the Strava API with Python to analyze Strava training activities. I started my winter training recently and wanted to make sure I’m training effectively. Especially, I want to see if I stay in the right zones during the training. I have set up my Forerunner alarms appropriate, but I train outdoor, so it goes up and down during the run and I can’t control everything during the ride. Human. Not robot.

I started with the stravalib library, for reading the data from the API. As usual in data science, I need to transform and prepare the data for visualization. I end up storing the generated plots and build a quick & dirty HTML-file to display the stuff. I will now guide you through the example. I will explain the important steps. You can find the complete code on https://github.com/marquies/strava-viz.

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Retrospective Q1/2017

What have I done:

  • The first quarter was much about migration from OpenShift PaaS to IaaS operating platform. We migrated all existing staging and production environments, with all customer data. A simple lesson learned: doing something over and over again trains you and you can perform much better if it counts.
  • In March Cloud of Things Starter Kit was launched.
  • Offsite, Offsite, Offsite. The growing demand of leaving the office building to getting to know each other, re-organize work and getting things done looks like an anti-pattern of work culture.
  • Late start of training for the upcoming MTB season.
  • Started programming an iOS App to refresh my developer skills.
  • Thought about some blog posts, but deleted them again. Some things should be untold.

Thought:

  • SMART defined goals are key.
  • Visualize your work.
  • Stop arguing about tools, start using one of them!
  • If you have more meetings than working hours a day, something is wrong.
  • Do you even have a target picture, bro?

Read:

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Retrospective January 2017

Slogan of the month: Stop starting and start finishing.

(Again)
What have I done:

  • Re-created the CoT project portfolio, now in a digital version (experimenting with Excel). It is good for sharing in our digital and distributed working environment, but a bit harder to update.
  • I attended the Bonn Agile Meetup January: “IoT und Bastelprojekte” and talked about my AirQuality Lab project 
  • I attended a lot of Kick-Off-events (unit, department, CoT-team, Service Development team). Some were useful, some had good food.
  • I worked on our first goals for 2017. First results in February 2017.
  • I completed a course on my Coursera data science specialization course (Reproducible research)
  • Watched the NFL playoffs

Read:

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The AirQuality Lab: How To Work With IoT Sensors

IoT Sensor: The AirQuality Lab

 

Want to learn about the Internet of Things and how to work with a sensor? I did too! I’ve been experimenting with the “Internet of Things” (IoT) since 2014 and have learned about the challenges with my sensor project: the AirQuality Lab. In the beginning, I just wanted to create something and work with the components. The scope of my side project was to read values from a sensor, transfer it to a thing-backend and then learning from the data. This post starts with the basic setup of the Thing and the following posts will cover further points.

After finishing a project in late 2014, I played around to learn a bit more about the Internet of Things (IoT) stuff for my next project. So, it was (and is) proposed, that “everything is connected in 2020” and I had to think about a product, product strategy and technical implementation in this area. Our thoughts about a domain model for IoT at this time was, to reduce everything to a source and a drain. Well, as hardware is cheap (but this is not all) I bought a Raspberry PI, a bunch of sensors and a small LCD display. Inspired by the CubeSensors, I wanted to measure air quality with the sensors and work with the derived data. This should help to understand how things process data, how to transfer it, how to analyze and derive information from it. I started the AirQuality Lab.

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