Continuous Machine Learning

Accelerating Data Science at scale



Continuous Machine Learning (CML) is a set of tools and practices that brings widely used CI/CD process to a Machine Learning workflow.

Using a validated approach to increasing the agility of software development CML helps organizations to integrate MLOps pipelines on top of the their existing technology stack.


for data science

Use GitLab to manage the end-to-end Machine Learning Lifecycle combined with DVC to enable full data, models and code version control and reproducibility

Flexible Deployment Options

Our CML solution can be easily integrated into your existing technology stack with both

On-premise or Cloud-based deployment options

Automated reporting

Auto-generate reports with metrics and plots in each Git Pull Request. Rigorous engineering practices help your team make informed, data-driven decisions


The AI Academy for you

We can enable programmatic access to your Machine Learning “back-end” by creating automated pipelines (“functions”) that simplify model experimentation and deployment while introducing continuous model performance and retraining 



Scale your ML Initiative


Automation Pipelines

  • 01

    Model Train

    This pipeline allows to automatically create models. Data scientists needs to do the versioning of the data or the experiment parameters

  • 02

    Model Evaluate

    This pipeline adds the ability to automatically diagnose and evaluate current models against new data

  • 03

    Model Retrain

    This pipeline adds the ability to automatically develop new models should the system detect model drift (lower performance of deployed model on new data)

  • 04

    Model Deploy

    This pipeline adds the ability to automatic deploy the champion model into the production environment making the process fully automated and achieving Continuous Machine Learning objective

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