DEEP LEARNING IN PRODUCTION BOOK
Design, develop, train, deploy, scale and maintain your Machine Learning models
Become a Machine Learning Engineer
Machine Learning gradually transforms more and more industries. Tech and non-tech companies are starting to incorporate AI into their business to outgrow their competition. Thus, machine learning engineering is one of the more sought after and well paid skills of today.
A Machine Learning Engineer can be described as a role that bridges the gap between data science and IT. A ML Engineer is much more than a guy who builds ML models. Depending on the company, his/her responsibilities also include:
Designing the entire ML lifecycle
Developing data processing pipeline
Training the models in cloud clusters
Deploying and maintaining large scale ML systems
It is estimated that 50% of IT leaders will struggle to move their AI projects past proof of concept (POC) to a production level of maturity. This is where you come into play.
After reading this book, you will be ready to build a fully-functional Deep Learning application. The background and skills that you will acquire from this book will provide you better job opportunities, will differentiate you from other data scientists and machine learning researchers and the most important thing: they will make you a better and more well-rounded engineer.
What will you learn?
how to design a deep learning system from scratch
how to structure and develop production-ready machine learning code
how to develop efficient and scalable data pipelines
how to optimize the model’s performance and memory requirements
how to make it available to the public by setting up a service on the cloud
how to scale and maintain the service as the user base grows
Is this book for you?
Are you a Deep Learning researcher with no software experience?
Are you a software engineer who is starting out with Machine Learning?
Do you want to become a Machine Learning Engineer?
If you are interested in learning how to take a simple model and transform it into a real-world application, then you came to the right place. Whether you are a software engineer, an ML researcher, a Data Scientist or an ML enginner, I'm sure that you will find value in this book.
Have a look at a free sample
Table of Contents
FAQ
Deep Learning in Production is a product of one year of effort. The pages and the code you will read, began as articles on our blog AI Summer and they were later combined and organized into a single resource. The reason I decided to invest the time in writing this book is very simple. The practices and principles mentioned here are what I wish I knew when I started my journey on machine learning.
Yes. The book goes really deep in software development techniques and frameworks so a solid programming experience is a requirement.You don’t need to be an expert programmer but understanding the fundamentals of Python is a must.
A lot of them. To give you a sneak peek, you will familiarize yourself with Tensorflow,Flask, uWSGI, Nginx, Docker, Kubernetes, Google cloud and more.
Everything can be executed in a single CPU without a problem. Besides, we will make extended use of the cloud so no fancy hardware is required.
The book is about 200 pages with a lot of code. With enough focus, it will take you a week. The average reader will need about two-three weeks.
Books purchased from the Kindle Store can be returned within seven days of purchase with 100% refund. Leanpub has a 45-day guarantee.
Sure. Feel free to support us.
You are free and even encouraged to use our content in your project, whether it’s for educational purposes, for fun, or for profit.
However, we do ask that you cite AI Summer by providing a link back to this page. You can also use the following format to cite us:
@article{aisummer, title = “Deep Learning in Production”, author = “Sergios Karagiannakos”, journal = “https://theaisummer.com/”, year = “2021”, url = “https://theaisummer.com/deep-learning-in-production-book/” }
Of course there is. You can find it here
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