Train your own NER tagger using transformer language models

Natural language processing is among the fields which are highly impacted by breakthroughs in the Deep Learning field. Hence, it is no surprise that multiple state-of-the-art techniques are pushing boundaries almost every day, and keeping up with the latest achievements, testing their capabilities has become increasingly difficult. In this post, we will focus on Named […]

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., […]

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 […]

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Do you want to know more about Data Science and AI? We wrote a whitepaper for those who want to embark on a Data Science journey. It will help you understand Data Science better and,  ultimately, help you make better business decisions. Whitepaper preview: UNDERSTANDING DATA What is a data-driven organization? Are all companies notdata […]