A list of my machine learning related publications

Machine Learning Books 📚

I had the unique opportunity to co-author two machine learning books. More to come …

“Building Machine Learning Pipelines” by O’Reilly Media

Building Machine Learning Pipelines book cover

Between 2019 and 2020, I co-authored the ML engineering publication “Building Machine Learning Pipelines”, published by O’Reilly Media.

Building Machine Learning Pipelines is an excellent resource for anyone looking to get a comprehensive understanding of the entire machine learning pipeline process. The book covers all stages of the pipeline, from data ingestion to model deployment, and even goes beyond that to include the optimization of model deployments, model privacy, and the design of model feedback loops.

One of the features of this book is the attention it gives to the early and late stages of the pipeline. Too often, books and courses on machine learning focus solely on model training and ignore the crucial steps of data ingestion, preprocessing, as well as model evaluation and deployment. Building Machine Learning Pipelines does a great job of explaining these stages and the importance of getting them right.

The book also covers the model deployment process. It walks readers through the different options available for deploying models, including using cloud platforms and containerization, and provides guidance on choosing the best option for a given situation. It also discusses the importance of monitoring and maintaining deployed models and provides tips on how to do so effectively.

One of the most impressive aspects of Building Machine Learning Pipelines is its coverage of model optimization. This is an often-overlooked topic in machine learning, but it’s critical for getting the most out of deployed models. The book provides a thorough overview of techniques such as A/B testing, and explains how to use them to optimize model performance.

Overall, Building Machine Learning Pipelines is an invaluable resource for anyone looking to understand and build effective machine learning pipelines. Its comprehensive coverage of the entire process, from data ingestion to model optimization, makes it an essential read for anyone looking to take their machine learning skills to the next level.

“NLP in Action” by Manning Publishing

NLP in Action book cover

“Natural Language Processing (NLP) in Action” is a comprehensive guide to the exciting field of natural language processing written in 2017. In this book, we took a dive deep into the fundamentals of NLP and explore the techniques and tools used in the industry.

First, we will cover the basics of NLP, including statistical methods such as bag of words and n-grams. These techniques allow us to extract meaningful information from large amounts of text data and are an essential foundation for many NLP tasks.

Next, we will delve into advanced techniques such as Word2Vec, which allows us to represent words as vectors in a continuous space and capture the meaning of words in a more intuitive way. This is a crucial step in many NLP applications, such as language translation and sentiment analysis.

We will also introduce deep learning, a powerful set of techniques that has revolutionized many fields, including NLP. We will cover convolutional and recurrent neural networks, which are commonly used in NLP tasks such as text classification and language modeling.

We will also explore the use of seq2seq networks, which are a type of neural network designed for tasks such as machine translation and text summarization. These networks can learn to map a sequence of input data to a sequence of output data, making them particularly well-suited for NLP tasks.

Finally, we will discuss the training of models on GPUs, which allows us to train large and complex models much faster than on traditional CPUs. This is particularly important in NLP, where the datasets can be very large and the models can be computationally intensive.

By the end of this book, you will have a solid understanding of NLP and the tools and techniques used to build real-world NLP systems. Whether you are a beginner looking to get started in NLP or an experienced practitioner looking to deepen your knowledge, this book is for you. By understanding the details of NLP and neural networks, it will make you appreciate transformers even more.