Services

MLOps

MLOps (Machine Learning Ops) is all about building the infrastructure and lifecycle around a Machine Learning model. It ensures that models are efficiently developed, managed and implemented in a production environment, with an eye on scalability, reliability and transparency.

About MLOps

Why is MLOps essential?

The data science market is evolving rapidly. More and more organizations are recognizing the power of data-driven work and investing in well-structured data storage. Developing machine learning models is one thing, but implementing them in production often proves challenging. Models are rarely immediately deployable (plug and play) and require careful management.

Important aspects such as reproducibility, governance, explainability and efficiency play a crucial role here. Moreover, the European AI Act reinforces the need to make machine learning processes more transparent and manageable.

With a well-established MLOps workflow, you benefit from:

Reliable and efficient implementation of machine learning solutions.
Reproducible and explainable models and predictions.
Continuous monitoring and performance monitoring of models in production.

MLOps infrastructure & lifecycle

MLOps is all about setting up the infrastructure & lifecycle around an ML model. This allows these models to be managed efficiently, allowing the models to perform reliably while also producing reproducible & insightful predictions. Key MLOps components include:

Experiment Tracking – For tracking and comparing model performance.
Data versioning – To ensure models are working with the correct datasets and to be able to retrace the dataset.
CI/CD for machine learning – Automated pipelines for model development and deployment.
Monitoring & drift detection – Continuous insight into model quality and anomalies.
Explainability – Transparency in model decisions and interpretation.

Details

Technical details

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