Abstract Machine learning has recently emerged as a powerful technique to increase
operational efficiency or to develop new value propositions. However, the translation of a …

In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

Machine learning systems are both complex and unique. Complex because they consist of
many different components and involve many different stakeholders. Unique because …

Organizations rely on machine learning engineers (MLEs) to operationalize ML, ie, deploy
and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …

Machine learning (ML) is now commonplace, powering data-driven applications in various
organizations. Unlike the traditional perception of ML in research, ML production pipelines …

Creating and maintaining a platform for reliably producing and deploying machine learning
models requires careful orchestration of many components---a learner for generating …

Today's data centers rely more heavily on machine learning (ML) in their deployed systems.
However, these systems are vulnerable to the data drift problem, that is, a mismatch …

Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …

The design patterns in this book capture best practices and solutions to recurring problems
in machine learning. The authors, three Google engineers, catalog proven methods to help …

The training, maintenance, deployment, monitoring, organization and documentation of
machine learning (ML) models–in short model management–is a critical task in virtually all …