Building Machine Learning Pipelines

Preface

Everybody’s talking about machine learning. It’s moved from an academic discipline to one of the most exciting technologies around. From understanding video feeds in self-driving cars to personalizing medications, it’s becoming important in every industry. While the model architectures and concepts have received a lot of attention, machine learning has yet to go through the standardization of processes that the software industry experienced in the last two decades. In this book, we’d like to show you how to build a standardized machine learning system that is automated and results in models that are reproducible.

What Are Machine Learning Pipelines?

During the last few years, the developments in the field of machine learning have been astonishing. With the broad availability of graphical processing units (GPUs) and the rise of new deep learning concepts like Transformers such as BERT, or Generative Adversarial Networks (GANs) such as deep convolutional GANs, the number of AI projects has skyrocketed. The number of AI startups is enormous. Organizations are increasingly applying the latest machine learning concepts to all kinds of business problems. In this rush for the most performant machine learning solution, we have observed a few things that have received less attention. We have seen that data scientists and machine learning engineers are lacking good sources of information for concepts and tools to accelerate, reuse, manage, and deploy their developments. What is ...