CML

Continuous Machine Learning

Accelerating Data Science at scale

 

WHAT IS CML?

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.

CI/CD

for data science

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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

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

On-premise or Cloud-based deployment options

Automated reporting

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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 

 

CML ADDED VALUES

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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