How DataChef helped Syngenta build a self-service Data Science Platform, and change the game with new ML products

an illustration to describe Syngenta

Who is Syngenta?

Syngenta is a global provider of agricultural science and technology. They specialize in solutions that transform how crops are grown and protected for the benefit of farmers, society, and the planet.

Syngenta is heavily investing in R&D in order to be the global leader in agricultural technology. They have a large team of data specialists that are working on innovative solutions all the time. In order to support these initiatives, Syngenta is building a data science platform on AWS that empowers specialists and increases their productivity.



Syngenta’s data science platform

As experts in data science on AWS, we have helped Syngenta by designing their data science platform in accordance with the best practices of doing data science projects at scale. We have introduced MLOps to streamline a data scientist's development flow. Rolling out our tagging strategy has helped with access and cost management. We have provided Syngenta with a Cost Overview dashboard that uses the aforementioned tags.


In general, our efforts led to the following:

  • Faster and more convenient onboarding of data scientists from different teams
  • Clear insight into costs


Machine Learning Products

One of the data science initiatives is a high-profile project concerned with querying environmental features from around the world that started in 2017. Due to its complex nature, the project was stuck in a POC phase. Execution times were slow, and the product could only handle one query simultaneously. We joined this project in 2021 to help tackle Syngenta's challenges. The project has become a huge success by moving the project to AWS and redesigning how certain key elements were calculated. We were able to deliver the project faster than expected, which resulted in new data science initiatives in terms of modeling, predictions, and optimization.


In general, our efforts on that ground led to the following:

  • Reduction of execution times by 83%
  • Unlimited scalability due to the serverless nature
  • A huge reduction in costs to operate the project

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