Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
Index

Deployment


Deployment into production is often seen as separate from the creation of models. At many companies, data scientists create models in isolated development environments on training, validation, and testing data that was collected to create models.

Once the model performs well on the test set, it then gets passed on to deployment engineers, who know little about how and why the model works the way it does. This is a mistake. After all, you are developing models to use them, not for the fun of developing them.

Models tend to perform worse over time for several reasons. The world changes, so the data you trained on might no longer represent the real world. Your model might rely on the outputs of some other systems that are subject to change. There might be unintended side effects and weaknesses of your model that only show with extended usage. Your model might influence the world that it tries to model. Model decay describes how models have a lifespan after which performance deteriorates...