Providing a holistic scientific approach to your Machine Learning project with Science as a Service
Machine learning projects, being of R&D nature, entail some risk and require specialist knowledge. For the implementation of an ML solution to be successful, it must be developed with both science and business in mind: making sure it's built for the right reasons, and allowing for the best use of the available tech.
It's our goal to bridge the gap between the scientific world and the business side of ML projects, translating the needs and objectives into technical requirements, and creating a roadmap for a viable machine learning project development.
Building quality analytical models helps companies “learn” from data, transforming information into actionable insights.
In Science as a Service, we combine engineering skills with scientific knowledge to bring you ML solutions that match your needs and objectives, and provide the best possible results.
We’re involved in your project from start to finish, providing necessary engineering support to address your needs.
Starting off with an in-depth workshop session, we can validate whether ML is the best solution out there and assess how to best answer your needs.
To take full advantage of ML, you have to match science, engineering, and business. Our team is there to make sure this mix works well for you.
See what steps are included in the Science as a Service process.
the model goes live
ML model training
A Proof of Concept (PoC) is the best way to start off a project, especially when the end result isn’t perfectly clear as it happens in R&D projects. Before you build a full-blown ML solution, you can test in on a smaller scale and see whether it’s viable.
The PoC is a mini-version of the whole Science as a Service process, consisting of the same steps, but requiring less data and performed within a limited timeframe to bring fast results: a validation of the ML solution.
This research project was conducted by our team in collaboration with academics from the Faculty of Geographical Sciences, University of Łódź, to help identify land abandonment from aerial photography.
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Building a machine learning Poof of Concept of a processing pipeline to find astrocytes in microscope images in collaboration with the Laboratory of Molecular Neurobiology, University of Warsaw.
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They show pride in their work that reflects that they care about the company's future versus just completing tasks.
Lead Engineer, Cancer Data Analytics Company
Their professionalism and vast knowledge of AI and ML were impressive.
They have an excellent ability to work remotely without it being a barrier. They fit seamlessly into our team.
CTO, Flexys Solutions