From Deep Learning Patterns and Practices by Andrew Ferlitsch
This article covers:
Feeding models training data in a production environment.
Scheduling for continuous retraining.
Using version control and evaluating models before and after deployment.
Deploying models for large scale on-demand and batch requests, in both monolithic and distributed deployments.
From Deep Learning Patterns and Practices by Andrew Ferlitsch
This article covers:
● Feeding models training data in a production environment.
● Scheduling for continuous retraining.
● Using version control and evaluating models before and after deployment.
● Deploying models for large scale on-demand and batch requests, in both monolithic and distributed deployments.
From Deep Learning Patterns and Practices by Andrew Ferlitsch
Like the best software engineering, modern deep learning uses a pipeline architecture based on reusable patterns.
Brian Goetz is one of the leading figures in the Java world. As Java Language Architect at Oracle, he helps steer the direction of the language’s evolution and its supporting libraries. He has led the language through several important modernizations, including Project Lambda. Brian has a long career in software engineering and is the author of the best-selling book “Java Concurrency in Practice.” (Addison-Wesley, 2006)
From Classic Computer Science Problems in Java by David Kopec
| Andrew Ferlitsch reveals new paradigms–and patterns–for automated deep learning
Andrew Ferlitsch, from the developer relations team at Google Cloud AI, is so far out on the cutting edge of machine learning and artificial intelligence that he has to invent new terminology to describe what’s happening in Cloud AI with Google Cloud’s enterprise clients. In this interview with editors at Manning Publications, he talks about the current and coming changes in machine learning systems, starting with the concept of model amalgamation. Ferlitsch is currently writing a book, Deep Learning Design Patterns, which collects his ideas along with the most important composable model components. |
From Interpretable AI by Ajay Thampi
From Grokking Artificial Intelligence Algorithms by Rishal Hurbans
What you’ll learn in this article:
§ The lifecycle of a genetic algorithm.
§ Designing and developing a genetic algorithm to solve problems.
§ The parameters for configuring a genetic algorithm lifecycle based on different scenarios, problems, and data sets.
From Grokking Artificial Intelligence Algorithms by Rishal Hurbans
