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 provides alternatives for innovation by 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 mission targeted on fixing one of many many environmental sustainability challenges dealing with the world immediately.
Contributors will likely be given entry to Cloudera Machine Studying operating 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 successful prototypes will likely 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 peculiar 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 Sensible Agriculture: With the world’s inhabitants anticipated to hit almost 10 billion by 2050, discovering sustainable methods to feed all of those individuals is important for addressing world starvation in addition to mitigating the local weather disaster. Local weather-smart agriculture (CSA) is an built-in method to managing landscapes — cropland, livestock, forests and fisheries — that handle the interlinked challenges of meals safety and local weather change. Machine studying (ML) has the potential to advance climate-smart agriculture by offering beneficial insights, predictions, and resolution help 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 help methods, local weather adaptation methods, and data-driven analysis.
- The Water Disaster: Whereas water is one thing many take with no consideration, its shortage is turning into probably the most urgent sustainability challenges for companies, governments, communities, and people world wide. Moreover being elementary 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 turning into a important concern. Doable tasks embrace forecasting water consumption primarily based on historic knowledge, climate knowledge, and inhabitants progress; utilizing satellite tv for pc imagery to detect modifications within the atmosphere which may point out underground leaks in massive pipelines; or predicting the quantity of rainwater that may be harvested in particular areas primarily based on climate forecasts and historic knowledge to help in designing efficient rainwater harvesting methods.
- Sustainable Cities: Cities are liable for 70 % of worldwide greenhouse gasoline emissions. That signifies that the local weather disaster will likely 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 large knowledge provide promise for growing the sensible cities of tomorrow. By bettering efficiencies and enabling higher decision-making, we will handle the sustainability challenges afflicting cities world wide. Doable tasks embrace air high quality prediction and monitoring, Predicting vitality demand in numerous components of town to optimize electrical energy distribution, or utilizing imagery to categorise waste varieties for extra environment friendly recycling processes.
For this Hackathon, members will likely be tasked with utilizing publicly accessible datasets (ideas for every theme are supplied) to create their very own distinctive Utilized ML Prototype (AMP) targeted on fixing or gaining additional perception right into a local weather or sustainability problem. Cloudera’s Utilized Machine Studying Prototypes are totally constructed end-to-end knowledge science tasks that may be deployed with a single click on straight 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 handle it as soon as and for all. Study extra about how one can take part within the hackathon right here.