Analytics at Scale is fast becoming a foundational element of operating models across financial services. Given the relatively untapped potential of Big Data, firms are looking to generate deep and impactful insights using techniques such as machine learning.
In today’s financial institutions, however, too many insightful data science and ML projects live in a Jupyter Notebook and die in a PowerPoint presentation. The insights fail to create value because the institution lacks the technical knowledge and infrastructure to properly integrate machine learning algorithms into day-to-day operations.
In this article, we explore the benefits and seven phases of Machine Learning Operations (MLOps), explaining how the approach can help overcome this key industry challenge and also how MLOps relates to the broader discipline of DevOps.
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