Services

MLOps

Machine Learning Ops (MLOps) is all about building the entire infrastructure and lifecycle around an ML model.

About ML E

Infrastructure & lifecycle

MLOps is all about building the entire infrastructure and lifecycle around an ML model. This allows these models to perform optimally, recognize patterns and make predictions so you can make informed decisions.

To put an ML model into production, there is often a standard process missing for it:

Putting the model live

Keeping track of model versions

Compare + monitor metrics

This comes at the expense of reproducibility, which can lead to substandard models and substandard data in production.
This creates several challenges:

How do we ensure that the right models enter production flawlessly and with minimal effort?
How do you guarantee the performance of the models when they are in production?
How do you use AI responsibly within current and future laws and regulations?
How do you make data inputs and model outputs traceable and reproducible?

MLOps solutions

We have MLOps solutions that solve these problems with a standardized approach:

Model versions and metrics to track and compare: during the training process, we log and compare model versions and the metrics so that we always choose the best model;
Standardized model deployment: with testing, on-demand infrastructure, CI/CD and different environments (development and production) we bring models into production;
Monitoring models in production: we perform statistical tests on various data, such as tabular, image, or text, and we detect any data and performance drift in a timely manner;
Automatic retraining of models: if monitoring indicates it is necessary, we can automatically retrain models;
Differentiate between your development, test, acceptance and production environments with sufficient computing resources and a robust workflow.
Track logging & alerts, keeping track of the performance of your models in production and sending alerts when action is needed.

By using our MLOps solutions, you make developing, putting into production and monitoring models easy for your data scientists.
The operation and performance of each model become insightful and manageable.
This allows us to work together more efficiently and effectively, resulting in better results.

Details

Technical details

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