Data and machine learning services

Premier

partnerships with GCP, AWS & Azure

10+ years

advanced analytics expertise

Best-in-class

partner network

Premier

partnerships with GCP, AWS & Azure

10+ years

advanced analytics expertise

Best-in-class

partner network

Data and ML services

As businesses grapple with the exponential growth of data and the need for intelligent data-driven decision-making, Grid Dynamics offers robust solutions to establish strong data engineering foundations. Our expertise spans implementing best practices for data quality, governance, observability, migration, and stream processing.

We design and build scalable, high-performance data and analytics platforms that provide a rock-solid infrastructure for advanced analytics, machine learning (ML) operations, and large language model (LLM) lifecycle management. With our MLOps and LLMOps capabilities, clients can operationalize ML and LLM models efficiently while ensuring maximum performance, reliability, and business impact. Whether you need to modernize legacy data architectures, unlock data silos, or gain a competitive edge through AI/ML innovations, Grid Dynamics delivers modern data management and engineering solutions tailored to your unique requirements.

Data platforms

Whether extending existing data lakes, modernizing legacy data architectures, or building a new platform from the ground up, our scalable, cloud-native architectures, integrating best-of-breed technologies, deliver flexible, modular solutions tailored to evolve with your changing data needs.

Analytics platform

Semantic layer

Machine Learning platforms

Deploy a modern, scalable machine learning platform that streamlines your entire model lifecycle, from data preparation to deployment, using automated pipelines and robust infrastructure to accelerate development and minimize technical debt.

ML platform

MLOps

LLMOps

Operationalize large language models at scale through enterprise-grade LLMOps platforms that ensure reliable performance, efficient model lifecycle management, and seamless integration with data and analytics infrastructures.

LLMOps platform

Data engineering

Enhance data quality, ensure robust data governance, and improve data observability along with services designed to manage data migration and stream processing efficiently, all tailored to boost your operational performance and decision-making accuracy.

AI-powered data analytics

GenAI data migration

Data observability

Data quality

Data governance

Stream processing

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Our data and ML platform technology partners

Case studies

Case Study

Case Study

Case Study

How a Fortune 500 manufacturer reduced time-to-market for industrial tools using a data observability framework

Case Study

Loss Prevention with AI-powered IoT analytics platform on AWS

Case Study

Case Study

Data and ML starter kits

Get started on your data and ML journey with our range of reference implementations, designed to streamline implementation and accelerate time-to-market

STARTER KIT

Machine Learning Platform Starter Kit for AWS

Build a production-ready, cloud-native machine learning platform within weeks on AWS cloud. Improve data accessibility and quality, increase speed to insights, and achieve significant ROI with our starter kit.

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STARTER KIT

Machine Learning Platform Starter Kit for Google Cloud

Build a production-ready, cloud-native machine learning platform within weeks on Google Cloud. Improve data accessibility and quality, increase speed to insights, and achieve significant ROI with our starter kit.

STARTER KIT

Analytics Platform Starter Kit for AWS

Build a production-ready, modern analytics platform within weeks on AWS cloud. Improve data accessibility and quality, increase speed to insights, and achieve significant ROI with our starter kit.

STARTER KIT

Analytics Platform Starter Kit for GCP

Build a production-ready, modern analytics platform within weeks on Google Cloud. Focus on business value first, while leveraging GCP managed services, lower infrastructure costs, and speed-to-market.

STARTER KIT

GenAI Data Migration Starter Kit

The GenAI Data Migration Starter Kit aims to minimize the time and complexity involved in code migration, upgrades, or replatforming projects. It utilizes modern LLMs for detailed code parsing, identifying dependencies, and transforming code into instructions or alternative snippets. It is also capable of generating tests for migrated entities, ensuring a smooth transition and reliable performance in the new environment.

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STARTER KIT

Data Observability Starter Kit

The GenAI Data Observability Starter Kit simplifies data quality onboarding for modern businesses by offering checks for tabular, structured, and unstructured data. It includes built-in quality assessments for null/missing values, statistical distributions, data freshness, volume, and anomaly detection through unsupervised learning models. Easily integratable into data platforms and modern data warehouses, this starter kit ensures a swift time-to-market for monitoring data quality across all data types.

STARTER KIT

Machine Learning Platform Starter Kit for AWS

STARTER KIT

Machine Learning Platform Starter Kit for Google Cloud

STARTER KIT

Analytics Platform Starter Kit for AWS

Build a production-ready, modern analytics platform within weeks on AWS cloud. Improve data accessibility and quality, increase speed to insights, and achieve significant ROI with our starter kit.

STARTER KIT

Analytics Platform Starter Kit for GCP

STARTER KIT

GenAI Data Migration Starter Kit

STARTER KIT

Data Observability Starter Kit

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Explore data and ML insights across industries

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All industries

Cross-industry

Learn how our data and ML services can be leveraged across multiple industries

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

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Transforming business process automation with retrieval-augmented generation and LLMs

In today’s competitive business environment, automation of business processes, especially document processing workflows, has become critical for companies seeking to improve efficiency and reduce manual errors. Traditional methods often struggle to keep up with the volume and complexity of the tasks, while human-led processes are slow, error-prone, and may not always deliver consistent results. Large

Semantic layer: Design principles and cloud-agnostic architecture

The diversity of modern data technologies leads to new challenges in establishing a consistent and accurate data view for data consumers. In light of this issue, a semantic data layer introduces a means of harmonizing a single point of view for business metrics, no matter how many different data storages or data consumer tools are

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

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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

Data quality control framework for enterprise data lakes

Data quality control is a critical capability for businesses, as data quality issues can disrupt processes, invalidate analytics, and damage a company's reputation. However, data quality control is often undervalued, and there is room for improvement in most enterprises. Grid Dynamics has developed a scalable and easy-to-extend data quality control framework that integrates with various data sources, performs data quality checks, and visualizes the results using open-source tools like Soda SQL, Kibana, and Apache Airflow.

Digital Commerce

Learn how our data and ML services create delightful e-commerce customer experiences

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8 trends and technologies driving the future of omnichannel digital retail

It should come as no surprise that Generative AI is the #1 tech trend in 2023, but it certainly isn’t the only one. Building a foundational composable commerce ecosystem of best-in-class tools to support and personalize customer buying journeys should remain top-of-mind for retailers and brands who want to take advantage of generative AI applications

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E-commerce trends for 2024

Explore the top 6 technology trends in e-commerce that blur the line between physical and digital experiences using a MACH-based flexible business model, AI-powered personalization, and unified customer data. Download this e-book to uncover how each trend promises tailored shopping journeys for every customer, and discover actionable steps to leverage them for business growth.

Manufacturing

Learn how our data and ML services can augment the manufacturing value chain

Supply chain resilience: A modular framework for sailing through disruption

In this white paper, we introduce a modular, “lego brick” approach to supply chain digital transformation for resilience and the technology framework elements.

Why you need a cloud-native analytics platform for smart manufacturing

Manufacturers are increasingly adopting cloud-native smart manufacturing analytics platforms to drive value and overcome common business process challenges. These platforms enable manufacturers to build more agile smart factories, make data accessible and actionable, monitor product quality in real-time, create resilient supply chains, and reduce costs.

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Driving the future of automotive manufacturing with cloud-native analytics

The commercial vehicle manufacturing industry has been held back by the limitations of enormous on-premises systems for far too long. And now, since the outset of the global pandemic, and the war in Ukraine, the sense of urgency to maintain availability, adapt to the changing supply chain landscape, and meet customer demands, is at an

Pharma and Life Sciences

Learn about our groundbreaking data and ML innovations in the pharma and life sciences sectors

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Digital health integration: Bridging the gap between technology and care

Achieve 100% digital health integration with strategic steps for interoperability, customer-centricity, equity, value-based reimbursement, and AI/ML innovation

Multimodal biomedical AI: Applications and challenges in pharma, MedTech, and healthcare

With the recent advancements in generative AI, and the ongoing explosion of multimodal biomedical data from large biobanks, electronic health records, medical imaging, and biosensors, it is now possible to capture the complexity of human health and disease more accurately. In this whitepaper, we’ll dig deeper into:

Financial Services and Insurance

Learn how our data and ML services are transforming the financial services and insurance industries

Harnessing IoT and AI for personalized and dynamic embedded insurance products

Discover how IoT and AI revolutionize embedded insurance. Achieve personalized coverage, real-time risk assessment, new revenue streams, & seamless integration

Insurance data 360: Sync your business data ecosystem, eliminate silos and see ultimate efficiency at scale

Having accurate information regarding customers' demographics, medical history, and socioeconomic status allows insurance companies to understand their clients well enough to offer protection tailored to their specific needs.

Insurance 360: Unlock value creation with these industry-disrupting, experience-refining trends

This whitepaper explores the three key areas that insurers need to focus on to create value in the insurance industry: developing a 360° customer view, modernizing analytics platforms in the cloud, and leveraging AI and ML to personalize customer experiences.

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Data and ML platform services

Data and ML services

Data platforms

Machine Learning platforms

LLMOps

Data engineering

Our data and ML platform technology partners

Case studies

Data and ML starter kits

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Get in touch

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