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Banking and financial institutions are constantly on the hunt to devour more data from their customers to offer better experiences. And eventually, to tackle the volume of data, and its complexity, banks have started leveraging Machine Learning (ML) models to penetrate deeper into data troves and uncover insights.
According to a study, the global market for machine learning in the banking sector is expected to be worth over USD 21.27 billion by 2031.
Though a hot trend, leveraging ML is also becoming a complex affair for banks, especially after the COVID-19 pandemic increased the diversity of financial instruments in the booming digital economy. However, there is no need for banks to search for the next big tech to manage their data, at least for the next couple of years. Instead, banks need to adopt MLOps as a strategy to overcome key challenges in handling their growing data complexity.
What Is MLOps?
For beginners, MLOps can be called a derived variant of DevOps targeting AI and ML projects exclusively. It standardizes and automates processes impacted or affiliated with ML projects and establishes a high degree of flexibility, transparency, and governance for...