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Authors: Alvaro Azabal Favieres, Senior Machine Learning Engineer and Hiru Ranasinghe, Machine Learning Engineer Generative AI is arguably the most ra...

Authors: Alvaro Azabal Favieres, Senior Machine Learning Engineer, Adham Al Hossary, Data Scientist and Pablo Carretero Álvarez, Data Scientist This i...

Authors: Theodoros Marinos, Machine Learning Consultant and Matthew Gela, Senior Data Scientist In recent years, there has been a growing concern abou...

At our Driving Analytics Adoption webinar, Datatonic had the pleasure of welcoming data experts, Rachel Purchase, Head of Data Product, Insights and D...

Introduction In recent years, many companies have looked to AI and Machine Learning as key technologies to drive growth by creating new revenue stream...

Datatonic acquires Canadian-based Cloud Analytics Consultancy, Montreal Analytics, to accelerate Global Expansion  17th April  Datatonic is pleased to...

Authors: Valentin Cojocaru, Data Science Lead, Matthew Gela, Senior Data Scientist, Christelle Xu, Machine Learning Strategy Lead AI and Machine Learn...

Authors: Kumail Kermalli, DevOps Engineer and Mohammad Pooladkhay, DevOps Engineer Background Cloud Build is Google Cloud’s server...

A guide by data leaders, for data leaders, looking to turn Data Mesh principles into practice on Google Cloud. The evolving nature of technology and r...

Building reliable and reproducible Machine Learning systems in production is a tough challenge that involves many facets beyond just the development...

MLOps is a set of best practices, guidelines and technologies designed to deploy and maintain ML models in production. According to a recent McK...

Author: Oliver Webb, Content Marketing Executive At our Data Mesh: From Principles to Practice webinar, Datatonic had the pleasure of wel...