MLOps done professionally

Continuous delivery and automation pipelines in machine learning done right

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Enabling Machine Learning At Scale

MLOps is the practice of bringing together Machine Learning (ML) and DevOps principles to automate the end-to-end ML lifecycle, from data preparation to model deployment and monitoring. This helps to ensure that ML models are developed, deployed, and maintained in a consistent and reliable way.

A partial list of logos of technology Gauss Algorithmic uses

MLOps The Gauss Way

Our 3 pillars of success from +50 projects in over 10 industries

Educating People

We believe that it is essential to educate the entire team about MLOps, from data scientists to engineers to operations staff. This helps to ensure that everyone understands their role in the ML lifecycle and can work together effectively.

Tailored Design

We take the time to understand our clients' specific needs and requirements before designing a MLOps platform. This ensures that the platform is fit for purpose and meets the needs of the business.

Enabling Collaboration

We believe that collaboration is essential for successful MLOps. We provide tools and processes that help to facilitate collaboration between data scientists, engineers, and operations staff.

MLOps Case Study

Telco MLOps

CEE Mobile Operator
Models to be deployed
engineers trained
kubernetes clusters

Gauss Algorithmic worked with the mobile operator to design and implement a MLOps platform built around mlFlow and Prefect on a shared Kubernetes platform. The platform was designed to be secure, scalable, and easy to use.

The implementation of the MLOps platform took just a few weeks, including training of the data science team. The mobile operator is now able to deploy ML models more quickly and reliably, and they are seeing a significant improvement in the performance of their ML models.

A screen shot of mlflow with telco workflows