Machine learning Ops

Consistently deliver actionable insights. Adopt DevOps ideas to machine learning and achieve greater business impact with automated machine learning operations.

Our clients

Finance & Insurance

Starter kits

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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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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How do we achieve efficient machine learning operations?

How to achieve efficient machine learning operations - Grid Dynamics

The MLOps process starts with data. Data scientists spend most of their time exploring, preparing, and ingesting data. The work continues with identifying features for machine learning models, versioning the data, and splitting it into training, validation, and test datasets. To increase the productivity of machine learning engineers, our blueprints focus on high accessibility of quality data for use with our powerful Analytics platform and ML platform.


Most of MLOps capabilities are focused on model lifecycle management. Data scientists are usually familiar with the first stages of the lifecycle, but face challenges during production deployment of models. The final stages of the lifecycle include packaging of the production model, versioning it, and saving it in a repository. From there, the production model is used to generate insights. The MLOps toolbox should support a variety of machine learning algorithms - from advanced analytics to neural networks and deep learning.


The last mile in MLOps involves model serving - a machine learning model is deployed to production as part of an application or microservice. The deployment options can include cloud, datacenter, or edge. The insight delivery can be done either via Model-as-a-Service or by embedding a model into the consumer application. The model lifecycle doesn’t end there though. The model performance is monitored and the model automatically retrained if needed, ultimately achieving autonomous model operations.

Machine learning operations industries

We develop advanced artificial intelligence use cases and implement automated machine learning operations processes for Fortune-1000 enterprises in various industries including telecom, retail, media, gaming, and financial services.

Read more about machine learning operations

5 technology enablers for DataOps
DataOps and MLOps are closely related concepts and every company needs both to increase the business impact of data analytics. Read this article to learn about how to improve data engineering and make data collection easier. Adopting DataOps in the company should also improve the culture in the data engineering team and make MLOps adoption easier.
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How to use GCP and AWS big data and AI cloud services from Jupyter Notebook
Jupyter Notebooks are integral parts of every ML Platform and any MLOps process. Unfortunately, they cover only a limited part of the MLOps process. When working with cloud-based platforms, data preparation and model deployment is done outside of the notebook with console or cloud API. We extended Jupyter Notebooks to allow machine learning engineers to work with big data and deploy models by accessible cloud APIs with a convenient DSL.
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Dell, VMWare, and Grid Dynamics join forces to accelerate the journey to AI
It’s hard to imagine efficient MLOps without a powerful machine learning platform. Read this whitepaper to learn about the open source-based ML platform that we created in partnership with Dell and VMWare to enable efficient deployment at the edge. We also prepackaged several real-world AI use cases to further increase the speed to insights.
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