Put AI into practice with Microsoft's Azure AI Hackathon
Published Mar 08 2021 09:52 AM 3,587 Views

If you’ve been looking for a reason to get started with AI to solve a particular problem or use case, look no further! We invite you to put your skills to the test and apply Azure AI to a new or existing project. As you may have seen in an earlier post by Anand Raman, we have been hosting an Azure AI hackathon in which you can submit your project and be eligible to win prizes. Developers of all backgrounds and skill levels are welcome to join and submit any form of AI project, whether using Azure AI to enhance existing apps with pre-trained machine learning (ML) models with Cognitive Services or building your own custom ML models with Azure Machine Learning.  


AI Hackathon homepageAI Hackathon homepage


If you’re interested in participating, visit the Azure AI Hackathon page to get started. The deadline is April 5th so you still have time to build and submit a project! Use one or more of the following Azure AI services to build a new project or update an existing project: Azure Machine LearningAzure Cognitive Services, Bot Framework and Azure Cognitive Search. Projects may integrate with other Azure services, open source technologies (including but not limited to frameworks, libraries, and APIs) and physical hardware of your choice. 


If you’re looking for a little inspiration, below are a few examples of past winners:


2019 First Place– Trashé

Trashe Smarter Recycling solutionTrashe Smarter Recycling solution



Submitted by Nathan Glover and Stephen Mott, Trashé is a SmartBin that aims to help people make more informed recycling decisions. While the idea is super impactful, it’s even more powerful when you see it in action- not just the intelligence, but the end-to-end scenario of how it can be applied in a real-world environment.

This team used many Azure services to connect the hardware, intelligence, and presentation layers—you can see this is a well-researched architecture that is reusable in multiple scenarios. Azure Custom Vision was a great choice in this case, enabling the team create a well performing model with very little training data. The more we recycle, the better the model will get. It was great to see the setup instructions included to help build unique versions of Trashé so users can contribute to helping the environment by recycling correctly within their local communities—this community approach is incredibly scalable.


2019 Second Place- AfriFarm



Niza Siwale’s app recognizes crop diseases from images using Azure Machine Learning service and publishes the findings so anyone can track disease breakouts. This also provides a real-time update for government agencies to act quickly and provide support to affected communities. As quoted by Niza, this project has an incredible reach to a possible 200 million farmers whose livelihoods depend on farming in Africa.

Creating a simple Android application where users can take photos of crops to analyze if each farmer is getting information when they need it, users can also contribute their own findings back to the community around them keeping everyone more informed and connected. Using the popular Keras framework along with the Azure Machine Learning service, this project built and deployed a good plant disease recognition model which could be called from the application. Any future work or improved versions of models can be monitored and deployed in a development cycle. From this, the progression of the model can be tracked over time.


2019 Third Place- Water Level Anomaly detector



Roy Kincaid’s project identifies drastic changes in water levels using an ultrasonic sensor that could be useful for detecting potential floods and natural disasters. This information can then be used to provide adequate warning for people to best prepare to major changes in their environment. Water Level Anomaly Detector could also be beneficial for long-term analysis of the effects of climate change. This is another great example of an end-to-end intelligent solution.


Roy is well skilled in the hardware and connection parts of this project, so it was brilliant to see the easy integration of the Anomaly Detector API from Azure Cognitive Services and to hear how quickly Roy could start using the service. Many IoT scenarios have a similar need for detecting rates and levels; in fact, Roy had hinted at coffee level detector in the future.  In a world where we all want to do our part to help the environment, it’s a great example of how monitoring enables us to measure changes over time and be alerted when issues arise.


These are just 3 of the past winners and submissions. For more inspiration, visit our gallery of past submissions.





Resources to get started

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