MLOps: Machine Learning Operations
Machine Learning Operations (MLOps) is a relatively new arrival to the world of data science. Barely five years old, it has already become viewed as a critical requirement for organizations in just about every industry and business sector that want to become model-driven by weaving data science models into the core fabric of their business.
However, organizations are finding that implementing MLOps at the enterprise level is a much more complex problem than just implementing MLOps for a few models or a single team. Scaling data science and MLOps practices swiftly, safely, and successfully across an enterprise requires a broader version of MLOps that encompasses the entire data science lifecycle and meets the requirements of various teams both now and in the future. Enterprise MLOps is a new, robust category of MLOps that solves this problem.
What is MLOps?
MLOps is a system of processes for the end-to-end data science lifecycle at scale. It provides a venue for data scientists, engineers, and other IT professionals, to efficiently work together with enabling technology on the development, deployment, monitoring, and ongoing management of machine learning (ML) models.
It allows organizations to quickly and efficiently scale data science and MLOps practices across the entire organization, without sacrificing safety or quality. Enterprise MLOps is specifically designed for large-scale production environments where security, governance, and compliance are critical.
How Did We Get Here: The Journey to Today's Enterprise MLOps
Until a decade ago, the majority of work done in machine learning (ML) was experimental due to limitations in computing power. As it became practical to process vast amounts of data, those companies that were able to transition experimental ML models into production reaped huge rewards – but these successes were exceptions, not the norm.
The majority of projects stumble when models are transitioned from the data scientists to the production engineers for a wide variety of reasons including the need to recode models into different languages for deployment (e.g. Python/R vs Java), inability to recreate the data used for training in production, and no standardization in the deployment processes.
This is because the majority of companies are still using what Deloitte describes as an "artisanal" approach to ML development and deployment. This lack of scalable patterns and practices delays the value of data science. This is borne out by the results of a recent survey by DataIQ where one-third of respondents reported that it took months to get models into production. Visibility into project projects is also limited, with over 45 percent of respondents providing no or periodic updates. In another survey, 47 percent of ML projects never get out of the testing phase. Of those that do, another 28 percent fail anyway.
To overcome these challenges the data science community looked to DevOps (or Development Operations) from the software engineering field for inspiration. Many of the concepts focusing on shortening development time, and increasing speed and quality were adopted. However, because data science and application development produce very different products, a new practice, MLOps, was born.