Everyone is a Data Scientist
The shortage of Data Science talent is well documented. Organizations face an uphill struggle to recruit (and retain) Data Scientists and, for now, we just must deal with this fact. But this situation is changing. Coding is regularly touted as an essential skill and is now a popular classroom activity with many children now taking […]
Can you mine your data?
UNDERSTANDING DATA What is a data-driven organization? Are all companies not data driven, to some extent? Why are we now labelling this as something new? Is it because we have now labeled data as a precious substance? When it comes to Data Science, there’s a lot of confusion. Everyone is jumping on the AI bandwagon. […]
Evaluating MLOps Tools
Machine learning lifecycle This section describes a generic pipeline, which is a common use case for real-world modeling initiatives. We also rely on this pipeline when testing existing tools and conducting our own experiments. It is based on Hapke H., Nelson, C. Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow (O’Reilly Media, Inc., […]
Data Quality
In this short video, our CEO Martin Haagoort explains the main attributes of data quality: uniqueness, completion, timeliness, and consistency. Improving the quality of your data information will lead to outstanding outcomes.
Definition of Artificial Intelligence
Artificial Intelligence is everywhere. It may seem complex, but the equation is simple: sort, filter, select. CEO of Intellerts, Martin Haagoort, gives his own definition of AI in a surprisingly quick and informative way.
onAIr – Artificial Intelligence broadcast
Would you like to keep up with all the latest advances in AI and learn all about what AI can dofor you? Don’t wait and sign up now for onAIr: a bimonthly interactive and creative broadcast devoted to Artificial Intelligence. onAIr episode #1 | Artificial Intelligence In this episode of onAir, we dive into the world of […]
Selecting your optimal MLOps stack: advantages and challenges
MLOps Principles In 2015, Google released an influential paper Hidden Technical Debt in Machine Learning Systems. This paper described most of the problems associated with developing, deploying, producing, and monitoring machine learning-driven systems. The paper revealed that ML is no longer a discipline for data scientists. It is also relevant for any software engineering practitioner […]