Your R&D team
on demand

Providing a holistic scientific approach to your Machine Learning project with Science as a Service

Bridging the gap between science and business

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.
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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.

Services

What we offer

ML workshops

2-days workshops with our engineers to assess how ML can help you achieve your goals

Science as a Service

An end-to-end process to build a viable ML solution for your business

Team extension

An end-to-end process to build a viable ML solution for your business

Data exploration workshop

You'll receive a recommendation on intruding ML into your data processing pipelines & a fast PoC

LLMs

Unleash the full potential of ChatGPT and similar solutions with our our prompt engineering expertise.

ML audit

Our engineers will identify problematic cases and suggest or even implement an alternative solution for you.

XAI implementation

Find out what's behind your model decision making process.

Science as a Service for your business

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.

The benefits of the Science as a Service process include:

End-to-end project support

We’re involved in your project from start to finish, providing necessary engineering support to address your needs.

Early idea validation

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.

Interdisciplinary team

To take full advantage of ML, you have to match science, engineering, DevOps and business. Our team is there to make sure this mix works well for you.

Process

See how it's done

See what steps are included in the Science as a Service process.

ML workshops

Exploring solutions

Data analysis

& preprocessing

Deployment

the model goes live

Training

ML model training

The ML team analyzes the data and searches for supplementary datasets. They suggest other relevant data sources, and perform feature engineering.

Discuss your business profile with our engineers, identify your pain points and the problem to be solved with ML, and define available data during a workshop session.

ML engineers select base models and modify them to suit the problem, train the chosen models and iterate to define the final configuration.

ML engineers and MLOps work on integrating the models with your systems, define the required infrastructure, implement the model, and present the results.

PoC in a Machine Learning project

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.

PORTFOLIO

Our projects

Finding land abandonment with the use of ML

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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Astrocytes instance segmentation with ML

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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