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The world faces a number of environmental sustainability challenges — from the local weather disaster and water shortage to meals manufacturing and concrete resilience. Overcoming these hurdles presents alternatives for innovation by means of expertise and synthetic intelligence.
That’s why Cloudera and AMD have partnered to host the Local weather and Sustainability Hackathon. The occasion invitations people or groups of information scientists to develop an end-to-end machine studying undertaking centered on fixing one of many many environmental sustainability challenges going through the world as we speak.
Contributors will probably be given entry to Cloudera Machine Studying working on AMD {hardware} to allow swift, highly effective computations and breakthrough improvements — a pairing that can assist knowledge scientists craft local weather and sustainability options. On the completion of this hackathon, each line of code from the profitable prototypes will probably be made public in order that the occasion can contribute to the collective effort to handle the local weather disaster and different urgent environmental sustainability challenges.
This isn’t your abnormal hackathon — it’s meant to yield actual, actionable local weather options powered by machine studying. Contributors can select from the next classes for his or her prototype:
- Local weather Good Agriculture: With the world’s inhabitants anticipated to hit almost 10 billion by 2050, discovering sustainable methods to feed all of those folks is vital for addressing international starvation in addition to mitigating the local weather disaster. Local weather-smart agriculture (CSA) is an built-in strategy to managing landscapes — cropland, livestock, forests and fisheries — that tackle the interlinked challenges of meals safety and local weather change. Machine studying (ML) has the potential to advance climate-smart agriculture by offering precious insights, predictions, and resolution assist to farmers, researchers, and policymakers. This consists of local weather modeling and prediction, crop yield prediction, pest and illness detection, irrigation administration, precision agriculture, soil well being evaluation, crop choice and rotation, carbon sequestration, provide chain optimization, resolution assist methods, local weather adaptation methods, and data-driven analysis.
- The Water Disaster: Whereas water is one thing many take as a right, its shortage is changing into some of the urgent sustainability challenges for companies, governments, communities, and people world wide. Apart from being basic to sustaining life, water is also integral for agriculture, manufacturing, and industrial processes. The local weather disaster is a water disaster, too. Because the planet warms, this results in elevated evaporation, altering and unpredictable precipitation patterns, rising sea ranges, and melting snow pack and glaciers, amongst different challenges. Addressing water shortage is changing into a vital difficulty. Attainable tasks embrace forecasting water consumption based mostly on historic knowledge, climate knowledge, and inhabitants development; utilizing satellite tv for pc imagery to detect modifications within the setting which may point out underground leaks in massive pipelines; or predicting the quantity of rainwater that may be harvested in particular areas based mostly on climate forecasts and historic knowledge to assist in designing efficient rainwater harvesting methods.
- Sustainable Cities: Cities are answerable for 70 p.c of worldwide greenhouse fuel emissions. That implies that the local weather disaster will probably be gained or misplaced in our city environments. Many of those emissions are pushed by industrial and transportation methods reliant on fossil fuels. However machine studying and massive knowledge provide promise for creating the good cities of tomorrow. By enhancing efficiencies and enabling higher decision-making, we will tackle the sustainability challenges afflicting cities world wide. Attainable tasks embrace air high quality prediction and monitoring, Predicting vitality demand in several elements of the town to optimize electrical energy distribution, or utilizing imagery to categorise waste sorts for extra environment friendly recycling processes.
For this Hackathon, members will probably be tasked with utilizing publicly out there datasets (options for every theme are supplied) to create their very own distinctive Utilized ML Prototype (AMP) centered on fixing or gaining additional perception right into a local weather or sustainability problem. Cloudera’s Utilized Machine Studying Prototypes are absolutely constructed end-to-end knowledge science tasks that may be deployed with a single click on immediately from Cloudera Machine Studying, or accessed and constructed your self through public GitHub repositories..
The local weather disaster gained’t wait — we hope you’ll be a part of us in utilizing the ability of information science and machine studying to assist tackle it as soon as and for all. Be taught extra about how one can take part within the hackathon right here.
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