InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
Getting Started with Quarkus
Quarkus created quite a buzz in the enterprise Java ecosystem in 2019. What exactly is Quarkus? How is it different from other technologies established in the market? How can Quarkus help me or my organization? To better explain the motivation behind the Quarkus project, we need to look into the current state of software development.
-
Key Takeaway Points and Lessons Learned from QCon London 2020
QCon returned to London this past March for its fourteenth year in the city, attracting over 1,600 senior developers, architects, data engineers, team leads, and CTOs. This article provides a summary of the key takeaways.
-
Spring Boot Tutorial: Building Microservices Deployed to Google Cloud
In this tutorial, the reader will get a chance to create a small Spring Boot application, containerize it and deploy it to Google Kubernetes Engine using Skaffold and the Cloud Code IntelliJ plugin.
-
Q&A on the Book AI Crash Course
The book AI Crash Course by Hadelin de Ponteves contains a toolkit of four different AI models: Thompson Sampling, Q-Learning, Deep Q-Learning and Deep Convolutional Q-learning. It teaches the theory of these AI models and provides coding examples for solving industry cases based on these models.
-
Has an AI Cyber Attack Happened Yet?
AI cyber attacks have happened and are happening, with increasing regularity. This article looks at recent attacks, the role of bots, and defense strategies you can employ.
-
The Kongo Problem: Building a Scalable IoT Application with Apache Kafka
In this article, author Paul Brebner discusses the best practices for developing IoT projects using Apache Kafka and Kafka Streams technologies and how to maximize Kafka scalability.
-
Q&A on the Book Agile Machine Learning
The book Agile Machine Learning by Eric Carter and Matthew Hurst describes how the guiding principles of the Agile Manifesto have been used by machine learning teams in data projects. It explores how to apply agile practices for dealing with the unknowns of data and inferencing systems, using metrics as the customer.
-
The Road to Artificial Intelligence: An Ethical Minefield
Increasingly-rapid developments in the field of AI have offered society profound benefits, but have also produced complex ethical dilemmas. Many of the most nefarious issues are often overlooked, even in the engineering community. There also exists the meta-ethical question of who ought to be the ones making decisions concerning the encoding of values into autonomous systems.
-
Why Visual AI Beats Pixel and DOM Diffs for Web App Testing
Visual AI breaks regions of pixels into rendered elements for comparison purposes, similar to how humans view web pages. As a result, Visual AI can compare any kinds of images on a page, providing a more effective mechanism for automated visual testing when compared to pixel and DOM diffing.
-
How King uses AI to test Candy Crush Saga
To be able to improve features in games which are constantly evolving, the challenge will be to scale tests to be on a par with new feature development. Automated tests are vital for King to keep up testing Candy Crush, therefore they are constantly looking for new improved ways to test.
-
Getting to Know Deep Java Library (DJL)
Amazon has announced DJL, an open source library to develop Deep Learning models in Java. This article details how to get started with the toolkit. The library aims to reduce number of software dependencies by enabling end-end Deep learning development in Java, rather than having to use additional technologies such as Python or R.
-
InfoQ Editors' Recommended Talks from 2019
As part of the 2019 end-of-year-summary content, this article collects together a list of recommended presentation recordings from the InfoQ editorial team.
CONTENT IN THIS BOX PROVIDED BY OUR SPONSOR
FEATURED CONTENT:
Strong Consistency in Databases. What does it actually guarantee?
Evaluating Hbase and NoSQL Solutions for Real-time System of Record
Product Comparison Report for Key-Value Stores
Real-time Big Data at Hyperscale for AI, ML and IoT – What's Needed?Benchmark Reports
Follow Aerospike on: 
