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DevOps for Machine Learning - Grid Dynamic

Accelerate speed to insights

Agile and DevOps have drastically improved the way companies deliver software. And we can use many of the same ideas to great effect in data science and machine learning models. By breaking down silos and adopting MLOps with end-to-end continuous integration, delivery, deployment, and training workflows, you can deploy working machine learning insights to production more frequently and increase your speed to market by 10x.

Machine Learning insights - Grid Dynamics

Empower data scientists

Machine learning models are only as good as the data that was used to train them. So MLOps always starts with the data. Data scientists and machine learning engineers spend the majority of their time trying to source and wrangle the right data as well as select the right features for model training. But investing in data accessibility and quality capabilities, as well as providing data scientists with a convenient way to work with big data can make a 10x difference in productivity and the accuracy of decisions.

Machine Learning Operations - Grid Dynamics

Consistently deliver actionable insights

Data driven companies can no longer afford to use disjointed manual data science processes. MLOps offers a blueprint for streamlining and automating all stages of machine learning: from data preparation to deep learning model development, training, validation, versioning, deployment, and monitoring. Automated machine learning lifecycle management with a powerful ML platform not only increases the productivity of data scientists, but helps companies scale machine learning efforts without a loss of efficiency or quality.

Design Artificial Intelligence Powered Applications - Grid Dynamics

Design AI-powered applications

With only 22 percent of companies successfully deploying models to production, the last mile of MLOps remains a difficult problem. This is when companies need to acquire ML engineering and software development skills to tune production models, develop microservices, deploy Model-as-a-Service in the cloud, embed models directly into the consuming applications, or deploy them at the edge. With the help of traditional continuous integration and continuous delivery approaches and a powerful ML platform, the last mile challenge can be easily solved.

Continuous training and monitoring with machine learning - Grid Dynamics

Keep models relevant and impactful

While DevOps deals only with code, MLOps has to deal with data. With changing environments, model performance can deteriorate. Two of the most critical MLOps capabilities include the ability to monitor deep learning model performance and automatically retrain the models. Automating the workflows and doing it regularly increases the productivity of machine learning engineers, improves the quality of decisions, and enables effective autonomous model 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 analytical data 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.

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.

Technology and media
Technology and media companies recognized the value of data years ago and have accumulated significant amounts of structured and unstructured data. They often experiment with advanced machine learning algorithms including deep learning and reinforcement learning. We have helped the best of them stay ahead of the competition with a robust MLOps process and platform that allows scaling of machine learning efforts quickly, faster deployment of models, model retraining in real time, and production of high quality insights.
Retail and brands
Retailers and brands have to move quickly to optimize the customer experience and back-office operations, including inventory and supply chain. They are quickly growing their machine learning teams but without the right culture, processes, and tools, models rarely get deployed to production. We have helped Fortune-1000 retailers increase speed to insights by implementing an MLOps process, deploying experimentation and ML platforms, and making high quality data available to their data science teams.
Financial services
Most banks and insurance companies are not new to advanced analytics and machine learning. However, they often started machine learning programs a long time ago, with most tools and processes they use now outdated. Since security and compliance remain critical concerns, they need an MLOps process that can support secure access to data with tokenization and masking. It must also enable robust model testing and monitoring of model performance and provide a variety of deployment options.

We use MLOps in all our AI projects. We can help you implement machine learning operations in your organization and power it with the modern ML platform. To get started, choose from the following engagement options and contact us to discuss the first steps.


We offer free half-day workshops with our top experts in machine learning operations to demonstrate the benefits of MLOps, discuss your machine learning strategy, challenges, optimization opportunities, and industry best practices.


If you have already decided to improve your machine learning process, but can’t commit to a large investment, we will help you identify an AI use case, onboard MLOps, and deliver tangible results together with your team in 4-8 weeks.


If you are committed to transforming your organization so you can start consistently delivering insights with AI and ML, we can start with a 2–3 week discovery phase to identify the top challenges, design the ML platform solution, draft the MLOps process, and create an implementation roadmap.

Want to get in touch with us? We are pleased to begin helping you.

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