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CASE STUDY

Valent U.S.A. LLC

Summary:
Use machine learning to forecast crop growth.
Tags:
Valent wanted to jump ahead of the competition and increase customers’ visibility into the lifecycle of their crop production to increase marketable-grade yield.

Challenges

Increase customers’ visibility into the lifecycle of their crop production to increase marketable-grade yield.

outcome

  • Valent’s customers gained the ability to see crop characteristics ahead of time in order to make more timely field management decisions.
  • Valent staff can now update their app without large teams and in a fraction of the time, saving time and money.
  • Valent has the framework and infrastructure in place to easily deploy many new machine learning solutions to serve their customers using a streamlined and consistent process.

Business case

Jumping ahead of the competition by offering customers foresight into their operations enables Valent to remain a market leader.

approach

Serverless Machine Learning
  • Using Serverless for MLops allows Valent data scientists to push new models to dev, staging, or production.
  • This means that data scientists are the owners of product reliability, ensuring that they maintain and guarantee the quality of their model’s results.
  • Turn around times are dramatically reduced using this architecture, as data scientists own the full data pipeline from storage, through feature engineering, model building/validation/tuning, and out the door to customers. There is no more waiting for outside teams to deliver value to customers.


Managed Services Instead of Manual Services
  • Using cloud-provider managed services for API access through automated gateway deployment, computation through serverless cloud functions, and CI/CD through hosted integration and deployment pipelines streamlines ML deployment so that data scientists spend their time optimizing models, not doing data engineering.
  • Deploying the entire web app consuming the results of the ML models using the same framework means that all services operate in the same isolated cloud environment. This fully isolates the dev, staging, and production environments for consistency and reliability.

user audience

  • Agricultural operators
  • Growers
  • Product Managers
  • Machine Learning Engineers
  • Data Scientists

services

  • Data science
  • Machine learning engineering
  • MLOps
  • DevOps
  • Back end engineering

project details

  • 2 months
  • 3 person team
  • Agile implementation
  • OKR framework