Climate and Sustainability Hackathon with Cloudera and AMD

2321 Registered Allowed team size: 1 - 4
2321 Registered Allowed team size: 1 - 4

Winners are announced.

starts on:
Oct 18, 2023, 04:05 AM ()
ends on:
Nov 27, 2023, 04:55 AM ()
starts on:
Jan 29, 2024, 05:05 AM ()
ends on:
Mar 07, 2024, 04:55 AM ()



Presenting the Climate and Sustainability Hackathon with Cloudera and AMD! Go solo or team up with your fellow data scientists to develop an end-to-end machine learning project focused on solving one of the many environmental sustainability challenges facing the world today. Participants will be given access to Cloudera Machine Learning running on AMD hardware to enable swift, powerful computations and breakthrough innovations. This unique pairing harnesses the best of software and silicon, giving you an unparalleled experience in crafting climate and sustainability solutions. At the Climate and Sustainability Solutions Hackathon, every line of code contributes to a brighter, more resilient tomorrow. Join us and be part of the change!


For this Hackathon, you are tasked with creating your own unique Applied ML Prototype (AMP) focused on solving or gaining further insight into a climate or sustainability challenge. You will be required to use only open source data for this project, as all AMPs rely solely on open source technology and datasets.

Cloudera’s Applied Machine Learning Prototypes are fully built end-to-end data science solutions that can be deployed with a single click directly from Cloudera Machine Learning, or accessed and built yourself via public GitHub repositories. AMPs enable data scientists to go from an idea to a fully working ML use case in a fraction of the time. It provides an end-to-end framework for building, deploying, and monitoring business-ready ML applications instantly.

Like any other AMP, your project should serve as an example to other data scientists for the best way to approach solving a problem with ML methodologies. Your project in its final state must be an end-to-end solution, with code to ingest data, wrangle that data, train and validate a model, deploy the model with an endpoint, and communicate the results of the model via an interactive web application.


While each theme provided is meant to serve as inspiration, we want you to come up with your own unique project. Don’t expect the problem statement and outcomes to be explicitly provided for you. Use these themes to guide you towards a project that will meaningfully impact our understanding of climate and sustainability issues.

Improving Carbon Stock Calculations

A carbon stock is the quantity of carbon contained in a “pool,” meaning a reservoir or system which has the capacity to accumulate or release carbon. In the context of forests, for example, this refers to the amount of carbon stored in the world’s forest ecosystem — primarily in living biomass and soil, but to a lesser extent also in dead wood and litter.

While critical to climate mitigation, carbon stock calculations can be inaccurate for a variety of reasons. Those reasons include:

  • Variability in carbon content: accurate measurements require comprehensive sampling and data collection which is resource-intensive
  • Incomplete data
  • Spatial and temporal variability
  • Measurement techniques
  • Scale and resolution
  • Human error
  • Assumptions and oversimplification
  • Uncertainty in models
  • Data availability and quality

How can we use machine learning methods to more accurately measure and utilize carbon stock?

Possible datasets:

Climate Smart Agriculture

Using machine learning methods to advance climate-smart agriculture (CSA) is essential for addressing global hunger and mitigating the climate crisis. Here are some potential projects and considerations for leveraging machine learning in CSA:

  • Climate modeling and prediction
  • Crop yield prediction
  • Pest and disease detection
  • Irrigation management
  • Precision agriculture
  • Soil health assessment
  • Crop selection and rotation
  • Carbon sequestration
  • Supply chain optimization
  • Decision support systems
  • Climate adaptation strategies
  • Data-driven research

To support these projects, consider utilizing the following datasets:

Machine learning can empower farmers, researchers, and policymakers to make informed decisions, optimize agricultural practices, and address the challenges of food security and climate change.

The Water Crisis

While water is something many take for granted, its scarcity is becoming one of the most pressing sustainability challenges for businesses, governments, communities, and individuals around the world. Besides being fundamental to sustaining life, water also is integral for agriculture, manufacturing, and industrial processes.

The climate crisis is a water crisis, too. As the planet warms, this leads to increased evaporation, changing and unpredictable precipitation patterns, rising sea levels, and melting snow pack and glaciers, among other challenges. Addressing water scarcity is becoming a critical issue.

Possible projects include:

  • Forecasting water consumption based on historical data, weather data, and population growth
  • Using satellite imagery to detect changes in the environment that might indicate underground leaks in large pipelines
  • Predicting the amount of rainwater that can be harvested in specific regions based on weather forecasts and historical data to aid in designing effective rainwater harvesting systems.

Possible Datasets:

Sustainable Cities

Cities are responsible for 70 percent of global greenhouse gas emissions. That means that the climate crisis will be won or lost in our urban environments. Many of these emissions are driven by industrial and transportation systems reliant on fossil fuels.

But machine learning and big data offer promise for developing the smart cities of tomorrow. By improving efficiencies and enabling better decision-making, we can address the sustainability challenges afflicting cities around the world. 

In this challenge, participants will apply machine learning to an urban sustainability challenge to create long-lasting solutions.

Possible projects include:

  • Air quality prediction and monitoring
  • Predicting energy demand in different parts of the city to optimize electricity distribution
  • Using imagery to classify waste types for more efficient recycling processes

Possible Datasets:

Choose Your Own Adventure

These are just a few ideas, if you have your own idea for a machine learning project using publicly available data that is focused on climate and sustainability issues, then we would love for you to submit your own idea!

Prizes USD 14000 in prizes

Additional prizes for other standout submissions

Main Prizes
1st Place
USD 8000
2nd Place
USD 4000
3rd Place
USD 2000

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Aadarsh Shetty at
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