Practical Weak Supervision

Chapter 1. Introduction to Weak Supervision

Deep learning is data-hungry. A great amount of data is required for training deep learning models. The growing popularity of deep learning approaches fuels the need for larger, labeled datasets. On the other hand, the trend that we are seeing is that large deep learning models often need more data to work better. Recent key artificial intelligence (AI) achievements would not have been possible without labeled data.

The cost of creating, cleaning, and labeling training data often is a significant expense in terms of both time and money. Furthermore, in some cases, privacy is a key requirement, and an “eyes-off” approach, wherein machine learning practitioners cannot directly inspect the data to provide labels, isn’t possible given sensitive and personally identifiable information (PII) in the data.

In this book, you will learn practical techniques that will enable you to leverage weak supervision in your machine learning projects and to address the challenges and high costs of acquiring a high-quality labeled dataset.

Before we do that, let’s learn about why weak supervision is important for practical machine learning (ML). In this chapter, we will share practical use cases on how weak supervision can help overcome some of the challenges of implementing ML in the enterprise. We will provide an overview of the different types of weak supervision approaches and introduce Snorkel, a new machine learning framework. Finally, we will cover ...