This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
DataEngineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “DataEngineers of Netflix” series, where our very own dataengineers talk about their journeys to DataEngineering @ Netflix. Kevin, what drew you to dataengineering?
DataEngineers of Netflix?—?Interview Interview with Dhevi Rajendran Dhevi Rajendran This post is part of our “DataEngineers of Netflix” interview series, where our very own dataengineers talk about their journeys to DataEngineering @ Netflix.
DataEngineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “DataEngineers of Netflix” interview series, where our very own dataengineers talk about their journeys to DataEngineering @ Netflix. What drew you to Netflix?
Netflix’s engineeringculture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. In the Efficiency space, our data teams focus on transparency and optimization.
Alvaro Garcia – Principal Software Engineer at Apiumhub, Ruggero Tonelli – Principal Site Reliability Engineer at Netquest, Felix Kerger – Principal Developer Advocate at King, Corrado Calzoni – Principal Software Engineer at Roche, Stephan Lagraulet – Head of Engineering.
In this article, Tariq King describes the metaverse concept, discusses its key engineering challenges and quality concerns, and then walks through recent technological advances in AI and software testing that are helping to mitigate these challenges.
Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like Data Scientists to concerns in reproducibility and dependency management. Machine learning is a powerful new tool, but how does it fit in your agile development? By Jay Palat.
Software testing, especially in large scale projects, is a time intensive process. Test suites may be computationally expensive, compete with each other for available hardware, or simply be so large as to cause considerable delay until their results are available.
2022 was another year of significant technological innovations and trends in the software industry and communities. The InfoQ podcast co-hosts met last month to discuss the major trends from 2022, and what to watch in 2023. This article is a summary of the 2022 software trends podcast.
There’s an increasing concern about the energy use and corresponding carbon emissions of generative AI models. And while the concerns may be overhyped, they still require attention, especially as generative AI becomes integrated into our modern life.
Have you ever wondered about systems based on machine learning? In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers itself. A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make.
This article presents how to use data science to detect wastes and impediments, and concepts and related information that help teams to figure out the root cause of impediments they struggle to get rid of.
DataEngineers were tempted by the pressure of the moment to give up on testing all together. There was no need for generating your own data; just take a percentage of production data. In many cases, these tasks ended up on the shoulders of the DataEngineers themselves. DataEngineer burnouts.
As AI drives innovationfrom predicting hurricanes to enhancing legal workflowsorganizations must prioritize ethical practices, transparency, and robust governance to safeguard sensitive data while navigating an evolving regulatory landscape.
We organize all of the trending information in your field so you don't have to. Join 49,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content