[ESSEC Knowledge] by Jeroen Rombouts - Professor at ESSEC Business School where he holds the Accenture Strategic Business Analytics chair.
Many organizations are investing in data and analytics to become more data-driven and incorporate AI, transitioning from descriptive analytics to predictive and prescriptive analytics. However, the challenge lies in changing the culture towards agility and experimentation, necessitating reskilling and training programs.
Historically, the software development industry transitioned from a linear waterfall approach to the more adaptable agile methodology, and further to DevOps, which bridged the gap between development and operations. Today, the question arises: what can DevOps offer in the era of AI?
AI and machine learning present unique challenges due to their data-centric nature, and MLOps (Machine Learning Operationalization) is introduced as a set of practices that combine DevOps, machine learning, and data engineering to deploy and maintain ML systems in production. MLOps standardizes processes, fosters experimentation, ensures rapid delivery, and addresses performance monitoring, concept drift, accountability, and compliance issues.
Despite data science training programs, MLOps principles are often not adequately covered, and business leaders may struggle to create an efficient development and operations environment for their data teams. The complexity of ML algorithms and the perception of them as black boxes create a gap between AI and business.
MLOps should involve all stakeholders in data-based solutions, and it is essential for future data leaders to acquire basic MLOps skills to break down the barriers between business and engineering teams in the realm of AI and data.
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