Our Data Scientists are passionate about developing and industrialising data and AI solutions.
Our experts use and master the latest data science technologies, and are open-source oriented
Data Science is an expert discipline which is always changing.
At Artefact, we stay ahead of the curve by using the latest discoveries in Machine Learning, data processing methods and algorithm development.
We help companies master:
- Computer Vision: we explore visual data (images, videos) to detect objects, people, themes and automatically generate new assets.
- NLP: we analyse textual data (including tweets, emails, invoices) to uncover new consumer insights, improve their operational efficiency and automate their response to clients.
- Forecasting: we leverage all time series (such as sales, IoT sensors) to predict future demand and forecast market share, and detect abnormal trends.
- Exploratory, Ethical and Explainable Data: we understand hidden insights and potential biases contained in multi-dimensional datasets and black box models.
- Optimisation: we improve the efficiency of complex production chains and reduce operating costs (including inventory control, network and traffic optimisation, and workforce allocation).
We also value research and development (R&D). Our Data Scientists work closely with academic laboratories and research centres, leveraging insights from their latest research papers and conferences to develop advanced AI algorithms.
To find out more about our points of view and expertise, read the ARTEFACT TECH BLOG hosted on Medium.
Underpinning our teams is our Artefactory, our unique set of open-source tools and libraries of codes that helps us move through every stage of the modelisation process.
To stay at the forefront of the latest innovations, we collaborate with other data scientists in the open-source community, sharing knowledge and insights to learn and grow together.
Discover more on our Github.
We think “product” first! We do not stop at the POC stage, we always go further until industrialisation and scalability
Fact: only 10% of AI algorithms are put into production. The rest stall at the ‘Proof of Concept’ stage.
At Artefact, we think differently. Our Data Scientists think ‘product first’; they are committed to working out how to industrialise software, before even considering how to fine-tune any AI layers. This approach lets us work seamlessly from POC to final product delivery.
At every step of the machine learning production chain, we ensure that our research in data science is invested towards providing a valuable and sustainable product and maximum ROI.
MACHINE LEARNING OPERATIONS METHODOLOGY (MLOps)Our Data Scientists follow a strict Machine Learning Operations methodology (MLOps) which applies software engineering best practices (such as versioning, testing, continuous integration and delivery).
Real life data science has nothing in common with clean and abstract Kaggle challenges.
Instead, at Artefact, we develop models integrated with complex data sources, which are embedded into larger ecosystems, facing strong production requirements.
Read more about our MLOps methodology HERE.
WE WORK IN FEATURE TEAMS TO BREAK SILOSIn most organisations, data science teams work in silos. Their services do not scale across the full value chain and, all too often, they create ‘black box’ solutions that very few people can understand.
At Artefact, we break these silos to reach common business goals. Our data scientists work collaboratively, in feature teams, alongside stakeholders such as business owners, software engineers, DevOps, and UX designers to ensure all objectives and priorities are taken into account.