ML platform

Get to insights faster and achieve greater business impact. Efficiently scale machine learning efforts in the enterprise while adopting MLOps and increasing the quality of insights. Deploy in the cloud, in the datacenter, or at the edge.

Our clients

Finance & Insurance

Starter Kits

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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Machine Learning Platform Starter Kit for GCP
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.
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A machine learning platform should support the end-to-end data science and machine learning lifecycle, facilitate collaboration between data analysts and data scientists, and enable the MLOps process. The main capabilities of the AI platform should include data ingestion, data preparation, and data exploration. It should also include feature selection, feature engineering, prototyping, experimentation, model training, validation, model testing, deployment to production, model serving, and monitoring.

A good platform should support a variety of machine learning algorithms including predictive analytics, deep learning, reinforcement learning, and the creation of various types of neural networks, etc. A data science and machine learning platform is typically an extension of an enterprise data analytics platform and should support a variety of integrations.

There are a variety of product vendors offering software as a service solutions. All major cloud providers have their own data science platform offerings. Good open source-based options exist too. Different options may work best for different companies, depending on their machine learning use cases, the maturity of the team, whether they are in the datacenter or in the cloud, and what cloud provider they’ve selected.

Our focus is on making the right choice for the right circumstances. We go beyond the deployment of the AI platform. We help you choose the right one, integrate it with the data lake or analytics platform, make the data available, onboard the MLOps process, train data scientists, implement a common library of machine learning models, and ensure that the data science process works smoothly from data to insights.
Choose ML Platform - Grid Dynamics


We have developed advanced artificial intelligence use cases, machine learning platforms, and onboard MLOps processes for Fortune-1000 enterprises across various industries including telecom, retail, media, gaming, and financial services.

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How to use GCP and AWS big data and AI cloud services from Jupyter Notebook
In this article, we demonstrate an extension to Jupyter Notebooks that we developed to integrate with cloud APIs. With our solution, data scientists can use cloud services to work with big data, prepare data by submitting jobs to cloud data lakes, and deploy models into cloud AI platforms for serving, all without leaving Jupyter Notebook.
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AI & Machine Learning Will Transform The Customer Experience
Read this article to learn how to offer protection tailored to customers’ specific needs and how to develop the products, pricing, and real-time service delivery solutions that their customers want with AI & Machine Learning.
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5 technology enablers for DataOps
Onboarding DataOps and MLOps are one of the key objectives of implementing a data science platform. A good ML platform should support the DataOps process and integrate a large analytics platform or a data lake. In this article, we describe the toolbox that companies need to accelerate the machine learning journey with the MLOps process.
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