Busy Wednesday, with lots of fun stuff planned for Google I/O next week. I’ll be there in person and happy to connect with any of you that will also be there. In the meantime, check out some terrific material from today, including smart thinking on reliable systems, cloud ROI, and all the wrong answers to the question “why am I doing this?”
[youtube-video] Archetypes for Reliable Systems – Steve McGhee & Ameer Abbas, Google. I thoroughly enjoy hearing these two talk about designing for reliability. Take a few minutes and listen to them explore this topic in ways you’ve probably not heard before.
[blog] Why Are You Doing This? (Wrong Answers Only). Ask yourself, why are you doing a particular thing at work? If any of your answers are the “wrong” answers listed here, consider a reset.
[blog] How to Streamline ML Model Deployment? Automated Sanity Checks. Here’s some perspective from Intuit’s engineering team into how they check their models during deployment.
[blog] The beginning of the end of the password. Google is turning on passkeys for accounts, and this will likely have a big impact on folks.
[blog] BBC: Keeping up with a busy news day with an end-to-end serverless architecture. Powerful story of processing lots of information in an effective way.
[blog] A list of programming playgrounds. Want to play with technologies with no setup work? Here are a bunch of playgrounds for trying programming languages, SQL, DNS, git, and more.
[article] Setting the Record Straight on Cloud Computing ROI. How do you assess the value of he public cloud? Here are some good criteria.
[blog] Buffer HTTP requests with Cloud Tasks. I’m always learning new things about our own services, let alone everything else. This is a powerful option for those who have a lot of intra-service traffic in their architecture.
[article] Moving .NET Apps to the Cloud. Video and transcript featuring smart folks talking about a wide variety of considerations for migrating .NET apps. A natural Azure-bias in the group, but the advice is solid overall.
[blog] eBay’s Blazingly Fast Billion-Scale Vector Similarity Engine. Having a lot of historical data is great, but if you’re going to give folks timely recommendations, you need a way to quickly update and retrieve that data. Here’s what eBay does.
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