IA & Machine Learning

Machine Learning has established itself as a key discipline within organizations, enabling them to extract value from their data through models that learn patterns and support decision making based on previous experience. Its application spans a wide range of opportunities with a direct impact on the business, including fraud detection processes, customer segmentation, demand prediction, process optimization and decision automation.

In an increasingly dynamic and digitized business environment, Machine Learning serves as a strategic enabler to improve operational efficiency, anticipate risks and generate sustainable competitive advantages. However, its adoption requires overcoming technical, regulatory and organizational challenges, such as interpretability, scalability or integration with existing systems.

The evolution toward more modular and automated approaches, supported by key concepts such as component modeling, is transforming the way organizations develop, validate and maintain their models, democratizing access to advanced techniques and accelerating their implementation.

Our practice

At Management Solutions, we have extensive experience in developing and implementing advanced analytics techniques, evolving from traditional modeling to the integration of more advanced Machine Learning techniques that fully leverage latent data insights.   

Through our R&D area, we drive the transformation towards more efficient, interpretable and scalable modeling by developing our own methodologies and tools. Our key initiatives include the ModelCraft™ ecosystem, which integrates two complementary solutions:

• ModelCraft™ - our end-to-end component-based modeling platform, designed to simplify and industrialize the entire model lifecycle.
• ModelCraft™ Library - a Python/PySpark library built on reusable analytical components and tested during development, enabling structured, standardized and accelerated model development, validation and production through robust, reproducible and auditable pipelines.

Together, these solutions provide a comprehensive approach to analytical industrialization, allowing technical and business teams to develop, validate and monitor models with full traceability, control and efficiency.

ModelCraft™ and ModelCraft™ Library can be deployed both in the cloud (AWS, Azure, GCP) and on-premise, taking advantage of distributed computing capabilities, automatic scalability and a open-source approach, while integrating seamlessly into existing corporate architectures.

Our service offering

• Component-based modeling methodology and code reuse.
• Development and validation of supervised and unsupervised Machine Learning models.
• Automation of the complete modeling cycle (MLOps).
• Acceleration of modeling projects.
• Flexible on-premise or cloud deployment.
• Integration with existing databases and systems.
• Model interpretability and automatic generation of documentation.
• Technical and regulatory validation (AI Act, XAI, ECB, EBA).
• Model risk reduction through traceability and control.
• ModelCraft™ enables modeling for non-technical users through its user-friendly no-code interface.