Preface

Implementing a product based on machine learning can be a laborious task. There is a general need to reduce the friction between different steps of the machine learning development life cycle and between the teams of data scientists and engineers that are involved in the process.

Machine learning practitioners such as data scientists and machine learning engineers operate with different systems, standards, and tools. While data scientists spend most of their time developing models in tools such as Jupyter Notebook, when running in production, the model is deployed in the context of a software application with an environment that's more demanding in terms of scale and reliability.

In this book, you will be introduced to MLflow and machine learning engineering practices that will aid your machine learning life cycle, exploring data acquisition, preparation, training, and deployment. The book's content is based on an open interface design and will work with any language or platform. You will also gain benefits when it comes to scalability and reproducibility.

By the end of this book, you will be able to comfortably deal with setting up a development environment for models using MLflow, framing your machine learning problem, and using a standardized framework to set up your own machine learning systems. This book is also particularly handy if you are implementing your first machine learning project in production.