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.

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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

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Flexible Deployment Options

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

On-premise or Cloud-based deployment options

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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

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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