Machine Learning Engineering


Engineering

Machine learning engineering puts a machine learning model into production. It builds on the work done by data scientists and can include:

  • Integration of the model with data sources, such as databases or streaming sources
  • Scaling the model so that it can handle multiple user requests at the same time
  • Designing user experience and integrating the model with user interface
  • Moving the model onto edge devices such as mobile phones
  • Creating pipelines with automated model deployment, CI/CD and MLOps

In other words, machine learning engineering builds models into products that can be confidently rolled out to users.

What Leverness experts can do for you

Build on the work done by data scientists and put a machine learning model into production:

  • Integrate models with static and streaming data sources
  • Scale models to handle multiple user requests at the same time
  • Moving models onto edge devices such as mobile phones
  • Use MLOps to automate model deployment

Engineering work done on Leverness

Add product recommendations to online toy store

Add a product recommendation component to an online toy store and connect it to the UI and the product and review database.

125 hours, $50/hour

Increase crop yield

Integrate a predictive model for soybean yield based on soil properties as measured by a EM38 sensor, weather patterns, crop rotation and other characteristics.

91 hours, $65/hour

Automatic defects detection

Detect out-of-tolerance and other defects in machined parts using image recognition.

172 hours, $110/hour