Machine Learning Engineering

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Machine learning is one of the driving technologies in the digital transformation, generating additional revenue throughout organizations. Furthermore, it creates new business models, improves performance, services, and products. Utilizing machine learning applications, as a supporting factor to human work, has typically resulted in increased productivity, fewer errors, and overall higher throughput. The Reasonance machine learning engineering service is designed towards building usable machine learning applications that benefit companies from all parts of the industry spectrum.

Evaluating Opportunities for Machine Learning

Reasonance is your partner throughout the entire machine learning adoption process. Our modular approach allows us to assist throughout the entire process no matter if your organization already has a use case or is yet to define one. If your company has not yet identified a use case to start the adoption of machine learning technology, our experts are there to review the organizational processes and create a road map for adoption. We then highlight the use cases with the highest potential for return of investment (ROI) while taking into account feasibility and required effort. If you already have a use case or a business problem, where you would like to use machine learning, our team will evaluate the potential approaches and match them to your available resources and assets. These include data, existing IT systems and environment, computational resources, and domain expertise. This process allows us to select the one solution with least amount of risk and highest expected ROI. Having identified a suitable solution proposal, the work shifts toward machine learning and data engineering.

Holistic Approach to Machine Learning Engineering

Building a machine learning application and taking it into production is a large and complicated task, that is daunting to many enterprises. Our machine learning and data engineers cover the process of planning, building, evaluating, integrating, and finally taking the application into production for you. Most large-scale machine learning projects are comprised of multiple tasks with a large number of requirements and artifacts which need to be created, managed and operationalized:

Steps, assets and Utilities in a machine learning project

Our team is trained and proven in tackling this complexity, ensuring your organization receives a solution according to specifications. Furthermore, we take care of the deployment in such a fashion to guarantee reliability even for mission-critical applications. We maintain a large stack of technologies to ensure that whatever your initial system is, our solution can be integrated directly, or via independent APIs.

Our Toolbox

We offer the standard machine learning toolbox including classification, regression, estimation, density estimation, clustering, deep learning, and reinforcement learning techniques. Furthermore, we have developed in-house techniques that allow us to train models with less data without being prone to typical problems such as overfitting or underperforming with slightly imperfect data. Typical applications include:

Integration of Machine Learning Solutions

To ensure the application achieves its potential, it has to be used, and more often than not, there are an internal resistance and challenges within organizations to adopt these new technologies. To deal with this issue among others, Reasonance designs and implements a change management strategy that is specific to your organizational needs, including (re-)training of operators/workers and creating new processes.

Interested? Get in touch with us.