Implementing MLOps in the Enterprise

Preface

As MLOps veterans, we have often seen the following scenario play out across enterprises building their data science practices.

Traditionally, when enterprises built their data science practice, they would start by building a model in the lab, with a small team, often working on their laptops and with a small, manually extracted dataset. They developed the model in operational isolation, and the results were incorporated manually into applications. Then, once the model was complete and predicting with accuracy, the true struggle of trying to bring it to production, to generate real business value, began.

At this point, the enterprise faced challenges such as ingestion of production data, large scale training, serving in real-time, and monitoring/management of the models in production. These hurdles would often take months to overcome, presenting a huge cost in resources and lost time.

The AI pipeline is siloed, with teams working in isolation and with many different tools and frameworks that don’t necessarily play well with each other. This results in a huge waste of resources and businesses not being able to capitalize on their investment in data science. According to Gartner, as many as 85% of data science projects fall short of expectations.

In this book, we propose a mindset shift, one that addresses these existing challenges that prevent bringing models to production. We recommend a production-first approach: starting out not with the model but rather by designing ...