Having fun @work: AWS GameDay

Joining an AWS Training allows you to learn new things for your daily work. Attending a training commonly happens in groups of up to 13 people and has more of a frontal teaching character. An alternative event are workshops are more practical and done in a small group. And now, a third solution brings teams and people together and plays a competitive game: AWS GameDays.

What is an AWS GameDay and how is it structured?

An AWS GameDay is a hands-on learning experience that simulates real-world examples within AWS. During a GameDay, teams of 1 up to 4 people solve challenges in different flavors like security, sustainability, MLOps, cost optimization, and more. Depending on the topic, they span a time of 1.5 and 8 hours. Each challenge has an unknown scoring system that won’t be public facing. Play it and find out more about it ;-)

AWS GameDay Scoreboard

A typical day starts with a meeting where every attendee joins an online meeting to get more information from Unicorn.Rental. When the kickoff meeting ends, each team switches to its team channel and gets its dashboard with the tasks, scoreboard, and all other relevant information for the day. Based on the maximum time for the GameDay, the game stops at a specified time, and everyone moves back to the initial meeting. Now, as everyone is back, we will have winners!

Why is tecRacer thinking about it?

We, as tecRacer, are a modern and flexible Managed Service Provider, Development, and Consulting company. So a lot of people have moved into their HomeOffices since the pandemic started. All meetings (even team meetings) moved into the online world. We still know each other, but personal interaction in an offline way still needs to be added. So a lot of side information (like unique ideas, private life, etc.) has become harder to sustain and is at risk of getting lost. Some of us love our home offices, while others have never used them and love to be in the office.

What about bringing each office into an interaction with fun? Right, run an AWS GameDay!

Tour de Machine Learning

To run the first internal GameDay ever, we had to decide which one to run. In our case, I started a short list of possible directions and created an internal poll for this. The largest interest was in MLOps. So the GameDay was chosen by my colleagues, and I only needed some more minor event preparations to be made. Everything for the GameDay was on the AWS side from now on. I just had to organize bringing the people together and finding fantastic prizes for the winner.

The starting signal was given on Friday, the 17th of March 2023, at 10:00 AM German local time. 34 attendees came together with different levels of knowledge (maybe half of them without any knowledge in that area), found themselves in 9 teams, and started playing!

The teams were so mixed that we were able to have colleagues from a lot of different locations in Germany (Hamburg, Duisburg, Hannover, Frankfurt), Switzerland (Geneva), Austria (Vienna), Portugal (Lisbon), and India. Yes, India. We had a colleague who decided to work for a single day to attend our AWS GameDay during a longer vacation. Only reading the different locations brings a massive smile to my face.

One of the benefits of playing a game is the team names:

  • Rainbow Riders
  • U3 - United Unicorn Union
  • Robot Unicorns
  • ChatGPT_power_users
  • Team Elastic
  • It’s just statistics
  • Team Y
  • Dummycorn_Ice_Cream
  • Unicorn Cloud Crusaders

Each team is allowed to choose its team name, so no one knows who is personally behind that name. When the game was closed, it was surprising to discover the people behind those great names.

The winners

…. and the winners are:

  • 1st Place: Unicorn Cloud Crusaders (Sebastian Möhn, Oliver Wolf, Gernot Glawe, Uwe Strahlendorf)
  • 2nd Place: Robot Unicorns (Chrishon Nilanthan, Roman Korneev, Anna Danilova, Alexey Vidanov)
  • 3rd Place: It’s just statistics (Jessica von Janta, Meike Liedtke, Ralf Neumann, Maurice Borgmeier)

AWS GameDay Winners


I received so much positive feedback from colleagues who were amazed about our AWS GameDay—even those who have already attended a public one. The feedback was so positive, and everyone, even the very experienced (more than five years working with the specific services), learned more than expected.

Everyone asked me about the next GameDay. Of course, it is in the works, but as you all know, there are multiple projects, vacation plans, and many more things to be brought together to find such a date. Right now, I plan on having a GameDay in September this year.

Contact me if you are also interested in starting an internal AWS GameDay.

— Patrick

Similar Posts You Might Enjoy

Reinforcement learning with Ray 2.x on Amazon SageMaker

A few years ago Amazon SageMaker introduced direct support for reinforcement learning (RL) through integration of RL-frameworks, including Ray. However, support has not been kept up to date and the supported versions are no longer what you might call current. - by Franck Awounang Nekdem

Understanding Iterations in Ray RLlib

Recently I’ve been engaged in my first reinforcement learning project using Ray’s RLlib and Sagemaker. I had dabbled in machine learning before, but one of the nice things about this project is that it allows me to dive deep into something unfamiliar. Naturally, that results in some mistakes being made. Today I want to share a bit about my experience in trying to improve the iteration time for the IMPALA algorithm in Ray’s RLlib. - by Maurice Borgmeier

Streamlined Kafka Schema Evolution in AWS using MSK and the Glue Schema Registry

In today’s data-driven world, effective data management is crucial for organizations aiming to make well-informed, data-driven decisions. As the importance of data continues to grow, so does the significance of robust data management practices. This includes the processes of ingesting, storing, organizing, and maintaining the data generated and collected by an organization. Within the realm of data management, schema evolution stands out as one of the most critical aspects. Businesses evolve over time, leading to changes in data and, consequently, changes in corresponding schemas. Even though a schema may be initially defined for your data, evolving business requirements inevitably demand schema modifications. Yet, modifying data structures is no straightforward task, especially when dealing with distributed systems and teams. It’s essential that downstream consumers of the data can seamlessly adapt to new schemas. Coordinating these changes becomes a critical challenge to minimize downtime and prevent production issues. Neglecting robust data management and schema evolution strategies can result in service disruptions, breaking data pipelines, and incurring significant future costs. In the context of Apache Kafka, schema evolution is managed through a schema registry. As producers share data with consumers via Kafka, the schema is stored in this registry. The Schema Registry enhances the reliability, flexibility, and scalability of systems and applications by providing a standardized approach to manage and validate schemas used by both producers and consumers. This blog post will walk you through the steps of utilizing Amazon MSK in combination with AWS Glue Schema Registry and Terraform to build a cross-account streaming pipeline for Kafka, complete with built-in schema evolution. This approach provides a comprehensive solution to address your dynamic and evolving data requirements. - by Hendrik Hagen