{"id":679993,"date":"2022-06-10T11:51:58","date_gmt":"2022-06-10T09:51:58","guid":{"rendered":"https:\/\/www.devoteam.com\/whitepaper\/from-data-lakes-to-machine-learning-platforms\/"},"modified":"2026-01-21T16:55:04","modified_gmt":"2026-01-21T15:55:04","slug":"from-data-lakes-to-machine-learning-platforms","status":"publish","type":"whitepaper","link":"https:\/\/devoteam.info\/sk\/whitepaper\/from-data-lakes-to-machine-learning-platforms\/","title":{"rendered":"From data lakes to machine learning platforms"},"content":{"rendered":"\n
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The data market is evolving very quickly.<\/strong><\/p>\n\n\n\n

Until recently, data engineering platforms were focused on processing information and routing data. Today, more than half of our customers are looking for a global platform that integrates data science layers with data lakes and data pipelines. <\/p>\n\n\n\n

The industrialization of tools for Data scientists and data engineers, which help them create models and put them into production, is now a clear market trend.<\/p>\n\n\n\n

Data lakes are now an asset whose best practices are well known and mastered. The situation is different in data science, where what could be considered as state-of-the-art is not yet set in stone. On these platforms, our work moves between R&D and industrialization. Our goal is to identify the best practices, apply them within such platforms and support our customers in their implementation. This is a transitional phase before further industrialization.<\/p>\n\n\n\n

In five years, platforms for the operation of machine learning will be (ML Ops) automatically deployed on AWS; the procedures will be standardised and there will be no more confusion to how to deal with them.<\/p>\n\n\n\n

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In this e-book, we give you an overview of the challenges in the field of data processing in 2021:<\/strong><\/strong><\/p>\n\n\n\n