Machine learning has become an increasingly important tool for solving complex problems in many industries, from finance to healthcare and retail. However, despite its growing popularity, the development of machine learning models is still often ad-hoc, with each team or individual approaching the process differently. This can lead to inconsistencies and inaccuracies in models, which can have severe consequences for the people and systems that rely on them. The result is models that take too long to develop and must be more robust to deliver high quality.
In light of these challenges, I am motivated to write this book to provide a comprehensive and standardized approach to developing machine learning lifecycles. This is a resource that data scientists, engineers, and other professionals can use to ensure that machine learning models are built consistently and fairly at highest pace.
In addition to providing a standardized process, I also want this book to be flexible enough to be adapted to different projects and organizations. By providing a flexible framework that can be adapted to various use cases, this book offers a timeless workflow for almost all machine learning projects.
The playbook is focused on the various aspects of a machine learning lifecycle (e.g., system design, model validation, deployment, etc.). It provides a standardized approach to execute all steps quickly and reliably by giving hands-on examples or suggesting tools that can be used. In addition, it is flexible enough to be adapted to different projects and organizations.
It can be seen as a comprehensive guide to the machine learning lifecycle that provides high-level guidance to optimize the processes of a machine learning project and enables
"From Data to Deployment" will enable you to build standardised machine learning lifecycles fast! You can use the framework for side project or within your organization