Delivering ML value
There are many books, videos, and lectures available on ML and its related topics. In this book, we will cover a more adaptive approach and show how open source software (OSS) can provide the basis for you and your organization to benefit from the AI revolution.
In later chapters, we will tackle the challenges behind operationalizing ML projects by deploying and using an open source toolchain on Kubernetes. Toward the end of the book, we will build a reusable ML platform that provides essential features that will help contribute to delivering a successful ML project.
Before we dig deeper into the software, we must have foundational knowledge, and we must know the practical steps required to successfully deliver business value with ML initiatives. With this knowledge, we will be able to address some of the challenges of implementing an ML platform and identify how they will help deliver the expected value from our ML projects. The primary reason why these promised values are not realized is that they don't get to production. For example, imagine you built an excellent ML model that predicts the outcome of football World Cup matches, but no one could use it during the tournament. As a result, even though the model is successful, it failed to deliver its expected business value. Most organization's AI and ML initiatives are in the same state. The data science or ML engineering team may have built a perfectly working ML model that could have helped the organization's business and/or its customers; however, these models do not usually get deployed to production. So, what are the challenges teams face that prevent them from putting their ML models into production?
