Accelerating enterprise data migrations: A GenAI recipe
Discover how GenAI revolutionizes enterprise data migrations, ensuring faster, more efficient transitions with minimal disruption and maximum precision.
LLMOps blueprint for open-source large language models
Read our ultimate LLMOps blueprint for open source large language models. Improve your AI projects with this easy-to-follow, expert-written guide.
Machine learning for optimized campaign delivery and performance: A Yieldmo case study
Yieldmo and Grid Dynamics created a scalable, configuration-driven ML Platform to address the challenges of high-volume, real-time advertising operations.
A scalable, configuration-driven machine learning platform
Co-created by Grid Dynamics Director of Data Engineering, Dmitry Mezhensky, and Yieldmo Head of Analytics and Data Science, Sergei Izrailev Introduction Yieldmo, a Grid Dynamics client, is an advertising platform that helps brands improve digital ad experiences through creative tech and artificial intelligence (AI). The company uses bespoke ad formats, proprietary attention signals, predictive format
LLMOps blueprint for closed-source large language models
Building solutions using closed-source large language models (LLMs), including models like GPT-4 from OpenAI, or PaLM2 from Google, is a markedly different process to creating private machine learning (ML) models, so traditional MLOps playbooks and best practices might appear irrelevant when applied to LLM-centric projects. And indeed, many companies currently approach LLM projects as greenfield
Analytics and ML platform modernization: A case study
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 gaming loyalty company improve business KPIs, such as reduced total cost of
The data estate modernization playbook: Seven steps for business transformation
In today’s data-driven world, efficiently managing data is critical to business growth and competitive advantage. However, many organizations struggle to extract maximum value from their data due to outdated data architectures that limit their ability to store, process, and analyze large volumes of data. To address these challenges and meet future needs, organizations need a
IoT platform: A starter kit for Azure
Learn more about the IoT Platform Starter Kit for Microsoft Azure, which provides best-in-class cloud-native services for IoT and a reference implementation to accelerate the delivery of applied IoT projects.
How to enhance MLOps with ML observability features: A guide for AWS users
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 capabilities for batch and real-time serving, data management, uptime, scalability and many other
Building an IoT platform in GCP: A starter kit
Grid Dynamics has developed a starter kit for building an IoT platform from scratch in Google Cloud Platform (GCP), specifically tailored for smart manufacturing enterprises. The kit includes modular components for data collection, deployment to the edge, IoT device management, and more, reducing the time-to-market for developing an IoT platform.
