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Make the most of your investment with our MLOps solutions

Empowering Real-World Applications with Artificial Intelligence

The scale of the global machine learning (ML) market skyrocketed from $15.44 in 2021 to $21.17 in 2022. More and more companies of different sizes are looking for AI solutions. Still, while Data Science and Architecture are some of the most challenging fields of the IT industry today, there is also a large gap between building ML algorithms and their practical deployment. Unsurprisingly, only 22% of the investments in ML have resulted in their successful implementation at the production level.

Why is this solution best for you?

higher productivity
minimized amount of human-induced errors
reduced costs
faster product delivery
shorter deployment time
highly reliable ML pipelines
successful collaboration between team members
deeper understanding
fast responsiveness

What problems does MLOps help to solve?


Flexible Deployment Options

With Devopsbay, you do not have to readjust your business to meet the stringent requirements of the limited deployment options of an ML project. We believe it is the software that has to be tailored to the business and production needs. Thus, making it compatible with the existing infrastructure is one of our highest priorities.


Deep Learning Resource Orchestration

Scalability is one of the most-wanted features in any modern software and AI-based tools offered by Devopsbay are designed for smart autoscaling. An intelligent scaling process understands the optimal amount of resources required for desired software performance and is capable of dynamic correction to fulfill the computational demands of a changing workload.


Elastic GPU Scheduler

A fixed GPU amount allocated to tasks featuring high resource consumption can significantly prolong their completion and make them less efficient. For Deep Learning, it means extended training time. An elastic GPU scheduler we offer is capable of dynamic modification for better matching the available resources to the exact needs of the training process.


Performance Monitoring

Automation is a key to cost-effectiveness, and we know some processes are ideal candidates for automation. Performance monitoring can be considerably improved by the automation of an incident management system that can handle a resource provision issue and allocate more resources if needed without human supervision. This functionality reduces downtime and related costs.


Why us?

At Devopsbay, our passion for AI and deep understanding of the challenges related to its adoption by software have developed into our core specialization based on the MLOps approach. After developing our own serverless AI operating system for data scientists, building and maintaining over 5 ML platforms for widely known brands such as DataRobot and Paperspace, we can ensure you:
we know how to make data preparation and feature engineering flawless
we minimize expenses while working with complex infrastructure
we leverage intricate ETL solutions and an iterative-incremental process
we successfully reduce engineering bottlenecks
our projects feature versioning and reproducibility for data consistency and correct performance
we offer robust custom platform development from scratch
Damian, MLOps Platform Engineer

Our employees about MLOps

At Devopsbay, we are enthusiastic about AI and ML development and eager to improve the solutions available to individual ML specialists as well as entire teams working on the deployment of complex AI functionality in enterprise applications.

Damian, MLOps Platform Engineer
Damian, MLOps Platform Engineer

Launching a powerful ML-based application with Devopsbay

Building ML software can be challenging with its costly, complex and large-scale infrastructure, design and development bottlenecks. Fortunately, we know ways to address these issues and make artificial intelligence work for you. At Devopsbay, we have established reliable guidelines and best practices for our ML-powered applications that helped us to complete demanding projects in collaboration with world-known brands of the AI world.


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