Machine Learning Operations

Building robust, flexible and automated machine learning systems: from development and training to production with MLOps.

MLOps (machine learning operations) is an umbrella term for several practices which aim to ensure scalable, reproducible and robust machine learning products, thereby encompassing entire life cycles of related software. It brings together modern approaches from software engineering and data engineering in order to solve typical problems occuring in various stages of a machine learning project.

Benefits & Technological Potential

Reproducible Experimental Phases and Model Development

eproducibility in ML projects is never static due to evolving training code, changing input data and numerous contributors. It may therefore be crucial to rely on a setup which allows to share reproducible experiments and their results across teams, enhancing the creativity and speed of the development process. Related frameworks should be integrated already in the early stages of your ML project in order to reduce future technical debt and assure maintainability.

Large-Scale Training, Monitoring and Validation of Models

or large-scale training, it is crucial to establish ML pipelines that ensure efficiency and stability. Equally important is a continuous monitoring of the model validity. To this end, model management tools allow to track training runs, perform model diagnosis based on various metrics and optimize hyperparameters in a fully transparent manner.

Secure Data Management and Data Protection

arious stages in the life cycle of an ML system requires fast, secure and flexible access to a large amount data. Moreover, it needs to be ensured that data storage solutions agree with policies such as GDPR. 

Automated Model Life Cycles: Speeding Up Workflow

he integration of MLOps tools into CI/CD pipelines leads to smoother transitions from development cycles to production environments: the manual work required to bridge the gap between model training (1), the evaluation of the performance of the trained model (2) and the final deployment (3) is efficiently minimized in this way.

What is MLOps?

In a now well known 2015 Google paper, the life cycle of complex ML systems was investigated in the context of so-called technical debt. This term is commonly used to describe potential problems in the overall process of operationalizing the development, deployment and maintenance in large-scale software projects.