Operationalization

Machine Learning Operations (MLOps) is a framework that helps data scientists manage AI systems effectively. It includes a set of processes and tools that help develop better models and ensure their reliability.

MLOps covers all important aspects of ML development, from the efficient management of data to the training process to ensure that models guarantee fairness and accuracy. It also plays a crucial role in evaluating models through extensive testing, successful deployment in the real world, continuous monitoring of model performance and maintenance of models to ensure smooth operation.

The main objectives of MLOps

Data management: MLOps ensures that data is managed efficiently, including data collection, cleaning and preparation. Careful data management is critical to produce accurate and meaningful models.

Training process: MLOps enables an optimised training process that maximises model performance. In particular, this includes documentation of training steps and results for each run. Automated training of models and the selection of appropriate algorithms and hyperparameters for the best possible results can also be part of this (see Automation).

Model evaluation: extensive testing and validation ensures that models are fair and accurate. This includes checking accuracy, bias and model performance in different scenarios.

Deployment and monitoring: MLOps enables smooth deployment of models in the production environment and their continuous monitoring. This allows potential problems to be detected and fixed early to ensure reliable performance.

Maintenance and updating: MLOps ensures that models are continuously maintained and updated to maintain their performance. This includes regular reviews, model updates and adaptation to changing requirements.

Democratisation of AI systems

By using MLOps, companies can ensure that their AI systems are managed effectively. It enables AI to be seamlessly integrated into business processes and helps to ensure that the models developed can be used reliably and successfully. MLOps is thus an essential part of the successful implementation of AI systems.

In order to implement this excellently, it is necessary to compile the right solution for the company's circumstances from a large number of tools available on the market for the individual processes of the framework. Through our comprehensive knowledge of MLOps tools, as well as targeted questions, we put together a framework that fits your requirements and that you can use in your application. We also work on simple, abstractable solutions that enable the democratisation of maintenance and servicing processes of AI systems.