From Experimentation To Enterprise Reality: Why Mlops Is The Backbone Of Production AI

Abstract

Machine learning has progressed from a research tool to becoming one of the core technologies of many companies. A growing number of companies are investing heavily in building and optimizing their machine learning capabilities so they have a competitive advantage, but despite this large investment in building high-performing machine learning models, they are struggling with operationalizing their models into production environments. This gap between model development and deploying the model to production creates numerous challenges for the organization. For this reason, MLOps has emerged as the foundational framework for closing the gap between model development and production. MLOps covers the full lifecycle of machine learning systems, from data ingestion through to the deployment and ongoing monitoring of the machine learning model. The requirements for production environments are for the machine learning systems to be automated, observable, and auditable. One of the main reasons for this is that over time, as data distributions and user behaviors change, so too do the models that the model has been developed using. Therefore, without established monitoring and retraining mechanisms, even accurate models will deteriorate over time without the user being aware of it. Another critical factor in regulated industries is that the governance of AI systems must be transparent, reproducible, and compliant; hence, MLOPs enable users to create versioned models, auditable datasets, and controlled deployment pipelines. Collaboration between multi-disciplinary teams of Data Scientists, Engineers, and Business Stakeholders is essential if sustainable AI operations are to be maintained. Machine learning system operation and use are unique. Software engineering practices must adapt to the special requirements of these systems. Organizations with advanced MLOPs capabilities possess the knowledge and skill to effectively manage, govern, and scale successful AI systems. The successful addition of machine learning to mission-critical workflows requires resilient and evolving machine learning systems that continue to deliver value to the organization. MLOPs provide the infrastructure to ensure that AI-based systems consistently deliver long-term value on an enterprise scale.