This blog post is co-authored by Guillermo Ribeiro, Sr. Data Scientist at Cepsa.
Machine learning (ML) has rapidly evolved from being a fashionable trend emerging from academic environments and innovation departments to becoming a key means to deliver value across businesses in every industry. This transition from experiments in laboratories to solving real-world problems in production environments goes hand in hand with MLOps, or the adaptation of DevOps to the ML world.
MLOps helps streamline and automate the full lifecycle of an ML model, putting its focus on the source datasets, experiment reproducibility, ML algorithm code, and model quality.
At Cepsa, a global energy company, we use ML to tackle complex problems across our lines of businesses, from doing predictive maintenance for industrial equipment to monitoring and improving petrochemical processes at our refineries.
In this post, we discuss how we built our reference architecture for MLOps using the following key

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