Home Big Data Cloudera and AMD Spur Information Scientists to Take Local weather Motion

Cloudera and AMD Spur Information Scientists to Take Local weather Motion

Cloudera and AMD Spur Information Scientists to Take Local weather Motion


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 gives alternatives for innovation via 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 knowledge scientists to develop an end-to-end machine studying mission targeted on fixing one of many many environmental sustainability challenges dealing with the world at this time. 

Members might 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 might 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 odd hackathon — it’s meant to yield actual, actionable local weather options powered by machine studying. Members can select from the next classes for his or her prototype:

  • Local weather Sensible Agriculture: With the world’s inhabitants anticipated to hit practically 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 method to managing landscapes — cropland, livestock, forests and fisheries — that deal with the interlinked challenges of meals safety and local weather change. Machine studying (ML) has the potential to advance climate-smart agriculture by offering priceless insights, predictions, and choice 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, choice help methods, local weather adaptation methods, and data-driven analysis.
  • The Water Disaster: Whereas water is one thing many take without any consideration, its shortage is turning into one of the crucial urgent sustainability challenges for companies, governments, communities, and people all over the world. 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 turning into a vital difficulty. Doable initiatives embody forecasting water consumption based mostly on historic knowledge, climate knowledge, and inhabitants progress; utilizing satellite tv for pc imagery to detect modifications within the atmosphere that 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 liable for 70 p.c of world greenhouse gasoline emissions. That signifies that the local weather disaster might be received 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 creating the good cities of tomorrow. By enhancing efficiencies and enabling higher decision-making, we will deal with the sustainability challenges afflicting cities all over the world. Doable initiatives embody 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, individuals might be tasked with utilizing publicly accessible datasets (strategies 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. Cloduera’s Utilized Machine Studying Prototypes are totally constructed end-to-end knowledge science initiatives that may be deployed with a single click on straight from Cloudera Machine Studying, or accessed and constructed your self by way of public GitHub repositories..

The local weather disaster received’t wait — we hope you’ll be a part of us in utilizing the ability of knowledge science and machine studying to assist deal with 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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