- Amazon: Paperback and Kindle
- Leanpub: Epub and Pdf
Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
What you will learn?
- Best practices to write Deep Learning code
- How to unit test and debug Machine Learning code
- How to build and deploy efficient data pipelines
- How to serve Deep Learning models
- How to deploy and scale your application
- What is MLOps and how to build end-to-end pipelines
Who is this book for?
- Software engineers who are starting out with deep learning
- Machine learning researchers with limited software engineering background
- Machine learning engineers who seek to strengthen their knowledge
- Data scientists who want to productionize their models and build customer-facing applications
What tools you will use?
Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud, Vertex AI
Book description
Deep Learning research is advancing rapidly over the past years. Frameworks and libraries are constantly been developed and updated. However, we still lack standardized solutions on how to serve, deploy and scale Deep Learning models. Deep Learning infrastructure is not very mature yet.
This book accumulates a set of best practices and approaches on how to build robust and scalable machine learning applications. It covers the entire lifecycle from data processing and training to deployment and maintenance. It will help you understand how to transfer methodologies that are generally accepted and applied in the software community, into Deep Learning projects.
It's an excellent choice for researchers with a minimal software background, software engineers with little experience in machine learning, or aspiring machine learning engineers.
- Designing a machine learning system
- Setting up a Deep Learning Workstation
- Writing and Structuring Deep Learning Code
- Data Processing
- Training
- Serving
- Deploying
- Scaling
- Building an End-to-End Pipeline
The books is based on an article series published in our blog "AI Summer" and they were later combined and organized into a single resource. Some were rewritten from scratch; some were modified to fit the book's structure. Plus, we added completely new material!
- Laptop set up and system design: https://theaisummer.com/deep-learning-production/
- Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation: https://theaisummer.com/best-practices-deep-learning-code/
- How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage: https://theaisummer.com/unit-test-deep-learning/
- Logging and Debugging in Machine Learning: https://theaisummer.com/logging-debugging/
- Data preprocessing for deep learning: https://theaisummer.com/data-preprocessing/
- Data preprocessing for deep learning (part2): https://theaisummer.com/data-processing-optimization/
- How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch: https://theaisummer.com/tensorflow-training-loop/
- How to train a deep learning model in the cloud: https://theaisummer.com/training-cloud/
- Distributed Deep Learning training: Model and Data Parallelism in Tensorflow: https://theaisummer.com/distributed-training/
- Deploy a Deep Learning model as a web application using Flask and Tensorflow: https://theaisummer.com/deploy-flask-tensorflow/
- How to use uWSGI and Nginx to serve a Deep Learning model: https://theaisummer.com/uwsgi-nginx/
- How to use Docker containers and Docker Compose for Deep Learning applications: https://theaisummer.com/docker/
- Scalability in Machine Learning: Grow your model to serve millions of users: https://theaisummer.com/scalability/
- Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly: https://theaisummer.com/kubernetes/
If you like our effort, don't forget to star the project :) It matters!