Deployment Strategies to make AI/ML Accessible and Reproducible

Abstract

In recent years, big data and machine learning has been adopted in most of the major indus- tries and most startups are leaning towards the same. As data has become an integral part of all companies, ways to process them i.e. derive meaningful insights and patterns are essential. This is where machine learning comes into the picture. The emerging age of connected, digital world means that there are tons of data, distributed to various organizations and their databases. Since this data can be confidential in nature, it cannot always be openly shared in seek of artificial in- telligence (AI) and machine learning (ML) solutions. Instead, we need integration mechanisms, analogous to integration patterns in information systems, to create multi-organization AI/ML sys-tems.

There are many efficient machine learning systems to process the huge amount of data and based upon the task in hand, yield results in real-time as well. But these systems need to be curated and deployed properly so that the task at hand performs efficiently. Machine learning (ML) models are almost always developed in an offline setting, but they must be deployed into a production environment in order to learn from live data and deliver value.

A common complaint among ML teams, however, is that deploying ML models in production is a complicated process. It is such a widespread issue that some experts estimate that as many as 90 percent of ML models never make it into production in the first place. For the relatively few ML models that do make it to the production stage, ML model deployment can take a long time, and the models require constant attention to ensure quality and efficiency. For this reason, ML model deployment must be properly planned and managed to avoid inefficiencies and time-consuming challenges. This paper aims to provide you with information on the model deployment strategies and how you can choose which strategy is best for your application as well as how these strategies could be Reproduce to make faster deployments.