MLOps practices for a development team
Our company in short
Reaktor is a global technology consultancy that builds category-defining digital products. Our clients include industry leaders such as Adidas, HBO, Supercell, Cathay Pacific, and KONE. We're known for embracing autonomy, optimizing for speed, and meticulously building the most high-performing, multidisciplinary teams. Today, we are 700 strong, with offices in six countries.
Our business problem and machine learning
As a consultancy company we are committed to forming and serving customers with high-performance teams that can deliver well-designed digital products. On team level work and design practices become of importance, as well as knowledge of state-of-the art tools and methods. Machine learning starts to be a part of any software architecture in different industry verticals.
There will be specialized machine learning experts in future, too, but it is foreseeable that the ML will become commoditized in software development. Our main concern is how the best software and design teams will work in the future, so that we can stay in our quality and value promise.
MLOps gives the technological and methodological background for incorporating ML into the solutions, but that is where our work just starts. Important questions are, for example: How the business cases, services, and solutions should be designed and lead, and how the organization should support this process? What are the value-creating design and development practices of software systems that include ML components? How to shorten the cycle times, lessen faults, and align work better? What kind of MLOps frameworks and software fit best in different contexts and industry verticals?
Our research
We have concentrated on researching and developing the co-operation between design and developer teams in the context of delivering solutions for healthcare. Our technical research has been, e.g., about data preprocessing, which is often an important link between the business environment and ML architecture. Here, the LLMs act as a novel way of extracting structured information from text and combining freeform and structured UIs. Much of the value created by the project is realized as elevated competence, and we have participated in training material creation. Part of our training material has been tried at Helsinki University.
Business benefits
In the context of IML4E the efforts in training material and OSS platform, and maturity assessment scheme have already boosted our teams’ capabilities. We have been running an internal ML / MLOps lab where leads, designers, developers, and data scientists create together applications, train each other, and enrich our offering. The lab constantly creates proof-of-concepts to demonstrate our offering. All in all, the program has made our ML delivery capability broader.
