GradientCI runs machine learning models in every pull request or commit and reports back model and host metrics so that your team can build reproducible, maintainable, and deterministic machine learning models. GradientCI supports GCP/AWS/Azure/ and on-premise installations.

With built-in integrations for TensorFlow model parsing automatically add valuable insights directly into your code.

Learn more about Gradient here.

Reproducible Machine Learning

Run any model and report back model performance metrics. Status checks let you confirm that your model performs as expected before you merge to master or deploy the model into production. Track metrics such as:

• Loss and Accuracy
• CPU/GPU utilization
• Training time

Modern MLOps

Continuously develop and deploy machine learning models with ease. Add GradientCI to a machine learning repo to gain valuable insights into model performance. Easily pass custom model data for any framework of your choosing.

Train / Evaluate / Deploy

Create continuously updated machine learning models. With triggers for model/data drift you can build sophisticated ML pipelines

Free for OSS

We welcome academics, researchers, students, and more to build their models with FREE GPU time. Dynamically scale up your deployments from single-node CPU to large-scale distributed GPU tasks. Choose our hosted solution or run in your public cloud VPC or on-prem.