Cloudera and AMD Spur Data Scientists to Take Climate Action

Cloudera and AMD Spur Data Scientists to Take Climate Action

The world faces multiple environmental sustainability challenges — from the climate crisis and water scarcity to food production and urban resilience. Overcoming these hurdles offers opportunities for innovation through technology and artificial intelligence.

That’s why Cloudera and AMD have partnered to host the Climate and Sustainability Hackathon. The event invites individuals or teams of 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 — a pairing that will help data scientists craft climate and sustainability solutions. At the completion of this hackathon, every line of code from the winning prototypes will be made public so that the event can contribute to the collective effort to address the climate crisis and other pressing environmental sustainability challenges.

This isn’t your ordinary hackathon — it’s meant to yield real, actionable climate solutions powered by machine learning. Participants can choose from the following categories for their prototype:

  • Climate Smart Agriculture: With the world’s population expected to hit nearly 10 billion by 2050, finding sustainable ways to feed all of these people is critical for addressing global hunger as well as mitigating the climate crisis. Climate-smart agriculture (CSA) is an integrated approach to managing landscapes — cropland, livestock, forests and fisheries — that address the interlinked challenges of food security and climate change. Machine learning (ML) has the potential to advance climate-smart agriculture by providing valuable insights, predictions, and decision support to farmers, researchers, and policymakers. This includes 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, and data-driven research.
  • 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; or 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. 
  • 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. Possible projects include air quality prediction and monitoring, Predicting energy demand in different parts of the city to optimize electricity distribution, or using imagery to classify waste types for more efficient recycling processes.

For this Hackathon, participants will be tasked with using publicly available datasets (suggestions for each theme are provided) to create their own unique Applied ML Prototype (AMP) focused on solving or gaining further insight into a climate or sustainability challenge. Cloudera’s Applied Machine Learning Prototypes are fully built end-to-end data science projects that can be deployed with a single click directly from Cloudera Machine Learning, or accessed and built yourself via public GitHub repositories..

The climate crisis won’t wait — we hope you’ll join us in using the power of data science and machine learning to help address it once and for all. Learn more about how you can participate in the hackathon here.

Cloudera Contributors
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Jacob Bengtson
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