How DataChef helped Syngenta build a self-service Data Science Platform, and change the game with new ML products
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.
Why were we there?
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 needs a data science platform on AWS that empowers specialists and increases their productivity.
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
How we helped Syngenta?
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.
We joined the environmental querying project in 2021 and started 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.
The impact we made
In general, our efforts led to the following:
- Faster and more convenient onboarding of data scientists from different teams
- Clear insight into costs
Technologies we used
AWS, SageMaker, Microservices, ML, DS, Python, SQL