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Welcome to Modern
Machine Learning

Our ML Services

Seamlessly Bridging the Gap between Machine Learning and Operations

At The Edge or Not

We provide end-to-end machine learning development processes to design, build, deploy, and monitor explainable AI applications that preserve data privacy regardless of application or location

Encryption by Default

Even if your project isn't at the edge, our solutions ensure your data is fully encrypted during transport, storage and deployment

Keep Your Data

Most edge solutions require a centralized data store where all training occurs. Our Edge Machine Learning workflows provide capable edge devices with state of the art ML without centralizing any data and preserving data privacy 

Size Matters

Our multi-node ML systems improve performance, increase accuracy, scale to larger input data and  evolve to handle various types of data.

Distributed Machine Learning

The traditional way of using integrated tools for data mining and research analysis is no longer practical since the data is too large to manage. In recent times, distributed ML is becoming a preferred approaches as it allows for larger data analysis.

We have constructed workflows for distributed ML systems that not only handle large data from multiple sources, but  also easily evolve over time based on varies changing needs such as data drift, concept drift and deployment and model throughout needs. 

We can work within your existing stack or you can choose one of our cloud solutions for your needs. 

Accelerated ML Ops

Every machine learning pipeline is a set of operations, which are executed to produce a model and getting a model into the real world involves more than just building. Bridging the gap between ML model building and practical deployments is still a challenging task. 

We offer both custom and our own pre-built solutions to help your business successfully deploy, monitor and maintain a productive and effective ML lifecycle without the need for expensive internal resources

Federated Machine Learning

Traditional ML methods use centralized data stored locally to train models; this can greatly impact privacy and regulatory issues. Our Federated Learning workflows and methods are decentralized and we never touch any of your valued data.


Given the proper setup, even training and deployment can be decentralized and even further ensuring that your data stays private.  

Machine Learning @ The Edge

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years and this has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is still a very new area of application.

We have designed methods and solutions for deploying applications to almost any type of edge device regardless of its architecture or your existing infrastructure. 

White Structure

We Integrate With Your Ecosystem

We have expertise in almost every programming language and development stack in use today and even some legacy systems. Regardless of the design of your existing system or stack or the need for a new solution altogether, we can help design and develop a solution that works best for your functional and non-functional needs. 

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