Opening book details…
Can I read Deep Learning in Production on EtoBox?
Deep Learning in Production by Sergios Karagiannakos is a nonfiction available to read on EtoBox.
What is Deep Learning in Production about?
Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples. 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. 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 sca
Who reads Deep Learning in Production?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Sergios Karagiannakos
- Publisher
- Leanpub
- Published
- 2022
- Language
- EN
- ISBN
- 9786180033779
- Category
- nonfiction
- Subjects
- Computer Science, Artificial Intelligence (Ai), Stem
More by Sergios Karagiannakos
Browse all works by Sergios Karagiannakos
Similar books
- Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI — Luca Massaron Mark Ryan (2025)
- Mastering Azure Machine Learning: Execute large-scale end-to-end machine learning with Azure, 2nd Edition — Christoph Korner; Marcel Alsdorf (2022)
- Learning TensorFlow : a Guide to Building Deep Learning Systems — Tom Hope, Yehezkel S. Resheff, and Itay Lieder (2017)
- Applications of Machine Learning and Deep Learning on Biological Data — Faheem Masoodi; Mohammad Quasim; Syed Nisar Hussain Bukhari; Sarvottam Dixit; Shādāb ʻĀlam (2023)
- Deep Learning Systems — Andres Rodriguez (2020)
- Deep Reinforcement Learning in Unity : With Unity ML Toolkit — Abhilash Majumder (2020)
