Manufacturers and suppliers commonly offer maintenance contracts to companies who purchase operation-critical equipment. A preventative maintenance application based on a machine learning (ML) model can be used to help avoid failures that could impact business. To build the model, the manufacturer or supplier must spend time, money, and effort. However, to eliminate the costs of a maintenance contract, the customer could duplicate the model and manage the maintenance without the supplier's assistance. To build a machine learning (ML) model for maintenance, an appropriate training set must be collected and labeled; the architecture and training parameters must be chosen for optimal accuracy–speed trade-offs for the algorithm; and computing time is required to train it.

The rush to adopt machine learning practices has become ubiquitous in every industry. Enterprise data scientists want their AI models to be deployed into production to propel their organizations forward. Meanwhile, IT is looking for ways to enable their data scientists to build, train, and deploy their models in a way that does not compromise the security of their organizations, while making efficient use of resources like costly GPU clusters. Anaconda, the creator of Conda, has adopted Kubernetes as the standard for building and deploying ML models at speed and scale in Anaconda Enterprise. Elizabeth Winkler has devoted her career to building SaaS products that leverage natural language processing and machine learning to extract meaning out of massive amounts of data.