MLOps, short for Machine Learning Operations, involves a set of important processes and automation techniques designed to manage every step of the machine learning process.

This includes creating models, gathering the right data, writing code, and deploying it effectively. By using MLOps, teams can work together more smoothly because everyone follows the same set of practices. These practices help make sure that the work can be repeated, is easy to grow when needed, and is dependable. In the constantly changing field of AI, using MLOps isn’t just a choice anymore – it’s a smart way to handle the challenges of modern data work and keep moving forward with new and reliable ideas. Our MLOps offering is here for you to get started.

Benefits of MLOps

  • Standardization and streamlining of ML life cycle
  • Risk Mitigation
  • Data Governance in the loop
  • Ensure that business goals remain stable
  • Detect data (or concept) drift
  • Increased transparency and enable responsible AI
  • Scalability


  • Model Versioning and experiment tracking
  • Performance monitoring
  • Promote models and support deployment patterns
  • Compliance gates
  • No 1 to 1 correspondence to code lifecycle (they are asynchronous)


  • Feature Engineering, training, inference, and quality checks are Data Pipelines
  • Data is crucial in any AI application
  • Compliance gates
  • Data versioning


  • Continuous Integration is extended to include testing and validating data, data schemas, and models.
  • Continuous Deployment of an ML training pipeline that should also deploy a model prediction service
  • Automatically retraining and serving the models.
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Eager to know more?

Then definitely get in touch with Wouter. He will be happy to schedule a first (virtual) meeting to discuss all your possibilities. Let’s get started!

Wouter Buckinx

Director .Data