Machine Learning platforms

In a dynamic world where advertising success depends on data-driven decisions, the partnership between Yieldmo, a digital advertising platform, and Grid Dynamics has birthed a groundbreaking solution–a scalable, configuration-driven Machine Learning Platform. This innovative platform addresses...

Co-created by Grid Dynamics Director of Data Engineering, Dmitry Mezhensky, and Yieldmo Head of Analytics and Data Science, Sergei Izrailev Yieldmo, a Grid Dynamics client, is an advertising platform that helps brands improve digital ad experiences through creative tech and artificial intellig...

MLOps and DataOps principles, such as infrastructure-as-a-code management, continuous integration and continuous delivery, proper monitoring, and a standard approach to working with data assets, are essential components of a modern data estate. In this case study, we show how we helped a global...

Adoption of machine learning (ML) methods across all industries has drastically increased over the last few years. Starting from a handful of ML models, companies now find themselves supporting hundreds of models in production. Operating these models requires the development of comprehensive capa...

With increasingly data-driven business ecosystems around the world, leading technology, retail, manufacturing, and finance companies turn to Grid Dynamics to build data and machine learning platforms with MLOps processes to translate data into business value, capture substantial value from improved...

Modern enterprises operate with tremendous amounts of data, and data management has become an integral part of  business decision-making processes, KPI management and reporting. However, getting advanced insights from data and ML models is a sophisticated process that many companies still stru...

To optimize promotions across various channels and countries, a video game publisher encountered challenges in accurately forecasting demand 24 months ahead and effectively managing new game releases. Grid Dynamics addressed these issues by developing robust demand forecasting models and what-if an...

Personalization and recommendation models are widely adopted by technology and entertainment companies as highly efficient tools for digital experience improvement and conversion optimization. The traditional model development and productization process, however, requires significant engineering...

The retail industry is embracing big data, analytics, and machine learning (ML) to improve customer engagement, optimize operations, and drive sales. Increasingly, we’re seeing business intelligence systems that were once based on historic data, offline modeling, and traditional reporting being r...