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      How we host Ars, the finale and the 64-bit future

      news.movim.eu / ArsTechnica · Wednesday, 9 August, 2023 - 13:00

    How we host Ars, the finale and the 64-bit future

    Enlarge (credit: Aurich Lawson | Getty Images)

    Greetings, dear readers, and congratulations—we've reached the end of our four-part series on how Ars Technica is hosted in the cloud, and it has been a journey. We've gone through our infrastructure , our application stack , and our CI/CD strategy (that's "continuous integration and continuous deployment"—the process by which we manage and maintain our site's code).

    Now, to wrap things up, we have a bit of a grab bag of topics to go through. In this final part, we'll discuss some leftover configuration details I didn't get a chance to dive into in earlier parts—including how our battle-tested liveblogging system works (it's surprisingly simple, and yet it has withstood millions of readers hammering at it during Apple events). We'll also peek at how we handle authoritative DNS.

    Finally, we'll close on something that I've been wanting to look at for a while: AWS's cloud-based 64-bit ARM service offerings. How much of our infrastructure could we shift over onto ARM64-based systems, how much work will that be, and what might the long-term benefits be, both in terms of performance and costs?

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      Hosting Ars, part three: CI/CD, or how I learned to stop worrying and love DevOps

      news.movim.eu / ArsTechnica · Wednesday, 2 August, 2023 - 13:00 · 1 minute

    Image of devops

    Enlarge / DevOps, DevOps, DevOps! (credit: ArtemisDiana / Getty Images)

    One of the most important things to happen in the evolution of development over the past many years is the widespread adoption of continuous integration and continuous deployment , or CI/CD. (Sometimes the "CD" stands for "continuous delivery," depending on who you're talking to.)

    It's a concept that jettisons a lot of older ideas about how systems should be managed and instead gives you a way to update code and integrate changes as live rolling deployments while ensuring that the new code is tested and slots in smoothly with stuff that's already running. A properly architected CI/CD pipeline means you can get code changes into production faster and with fewer errors. But what does that look like in practice?

    It looks like Ars Technica, because we've adopted a CI/CD workflow to take full advantage of the flexibility afforded us by serverless cloud hosting. Welcome to part three of our four-part series on how we host Ars—here, we’re going to swing away from the "ops" side of "DevOps" and peer more closely at the "dev" part instead. Join us for a look behind the curtain at how Ars uses CI/CD in both our deployed applications and our infrastructure management!

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      How we host Ars Technica in the cloud, part two: The software

      news.movim.eu / ArsTechnica · Wednesday, 26 July, 2023 - 13:00 · 1 minute

    Welcome aboard the orbital HQ, readers!

    Enlarge / Welcome aboard the orbital HQ, readers! (credit: Aurich Lawson | Getty Images)

    Welcome back to our series on how Ars Technica is hosted and run! Last week, in part one , we cracked open the (virtual) doors to peek inside the Ars (virtual) data center. We talked about our Amazon Web Services setup, which is primarily built around ECS containers being spun up as needed to handle web traffic, and we walked through the ways that all of our hosting services hook together and function as a whole.

    This week, we shift our focus to a different layer in the stack—the applications we run on those services and how they work in the cloud. Those applications, after all, are what you come to the site for; you’re not here to marvel at a smoothly functioning infrastructure but rather to actually read the site. (I mean, I’m guessing that’s why you come here. It’s either that or everyone is showing up hoping I’m going to pour ketchup on myself and launch myself down a Slip-'N-Slide , but that was a one-time thing I did a long time ago when I was young and needed the money.)

    How traditional WordPress hosting works

    Although I am, at best, a casual sysadmin, having hung up my pro spurs a decade and change ago, I do have some relevant practical experience hosting WordPress. I’m currently the volunteer admin for a half-dozen WordPress sites, including Houston-area weather forecast destination Space City Weather (along with its Spanish-language counterpart Tiempo Ciudad Espacial ), the Atlantic hurricane-focused blog The Eyewall , my personal blog, and a few other odds and ends.

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      Behind the scenes: How we host Ars Technica, part 1

      news.movim.eu / ArsTechnica · Wednesday, 19 July, 2023 - 13:00 · 1 minute

    Take a peek inside the Ars vault with us!

    Enlarge / Take a peek inside the Ars vault with us! (credit: Aurich Lawson | Getty Images)

    A bit over three years ago, just before COVID hit, we ran a long piece on the tools and tricks that make Ars function without a physical office . Ars has spent decades perfecting how to get things done as a distributed remote workforce, and as it turns out, we were even more fortunate than we realized because that distributed nature made working through the pandemic more or less a non-event for us. While other companies were scrambling to get work-from-home arranged for their employees, we kept on trucking without needing to do anything different.

    However, there was a significant change that Ars went through right around the time that article was published. January 2020 marked our transition away from physical infrastructure and into a wholly cloud-based hosting environment. After years of great service from the folks at Server Central (now Deft ), the time had come for a leap into the clouds—and leap we did.

    There were a few big reasons to make the change, but the ones that mattered most were feature- and cost-related. Ars fiercely believes in running its own tech stack, mainly because we can iterate new features faster that way, and our community platform is unique among other Condé Nast brands. So when the rest of the company was either moving to or already on Amazon Web Services (AWS), we could hop on the bandwagon and take advantage of Condé’s enterprise pricing. That—combined with no longer having to maintain physical reserve infrastructure to absorb big traffic spikes and being able to rely on scaling—fundamentally changed the equation for us.

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      Machine learning, concluded: Did the “no-code” tools beat manual analysis?

      news.movim.eu / ArsTechnica · Monday, 15 August, 2022 - 13:00

    Machine learning, concluded: Did the “no-code” tools beat manual analysis?

    Enlarge (credit: Aurich Lawson | Getty Images)

    I am not a data scientist. And while I know my way around a Jupyter notebook and have written a good amount of Python code, I do not profess to be anything close to a machine learning expert. So when I performed the first part of our no-code/low-code machine learning experiment and got better than a 90 percent accuracy rate on a model, I suspected I had done something wrong.

    If you haven't been following along thus far, here's a quick review before I direct you back to the first two articles in this series. To see how much machine learning tools for the rest of us had advanced—and to redeem myself for the unwinnable task I had been assigned with machine learning last year—I took a well-worn heart attack data set from an archive at the University of California-Irvine and tried to outperform data science students' results using the "easy button" of Amazon Web Services' low-code and no-code tools.

    The whole point of this experiment was to see:

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      Setting our heart-attack-predicting AI loose with “no-code” tools

      news.movim.eu / ArsTechnica · Tuesday, 9 August, 2022 - 13:00 · 1 minute

    Ahhh, the easy button!

    Enlarge / Ahhh, the easy button! (credit: Aurich Lawson | Getty Images)

    This is the second episode in our exploration of "no-code" machine learning. In our first article , we laid out our problem set and discussed the data we would use to test whether a highly automated ML tool designed for business analysts could return cost-effective results near the quality of more code-intensive methods involving a bit more human-driven data science.

    If you haven't read that article, you should go back and at least skim it . If you're all set, let's review what we'd do with our heart attack data under "normal" (that is, more code-intensive) machine learning conditions and then throw that all away and hit the "easy" button.

    As we discussed previously, we're working with a set of cardiac health data derived from a study at the Cleveland Clinic Institute and the Hungarian Institute of Cardiology in Budapest (as well as other places whose data we've discarded for quality reasons). All that data is available in a repository we've created on GitHub, but its original form is part of a repository of data maintained for machine learning projects by the University of California-Irvine. We're using two versions of the data set: a smaller, more complete one consisting of 303 patient records from the Cleveland Clinic and a larger (597 patient) database that incorporates the Hungarian Institute data but is missing two of the types of data from the smaller set.

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      No code, no problem—we try to beat an AI at its own game with new tools

      news.movim.eu / ArsTechnica · Monday, 1 August, 2022 - 13:00 · 1 minute

    Is our machine learning yet?

    Enlarge / Is our machine learning yet?

    Over the past year, machine learning and artificial intelligence technology have made significant strides. Specialized algorithms, including OpenAI's DALL-E, have demonstrated the ability to generate images based on text prompts with increasing canniness. Natural language processing (NLP) systems have grown closer to approximating human writing and text. And some people even think that an AI has attained sentience . (Spoiler alert: It has not .)

    And as Ars' Matt Ford recently pointed out here , artificial intelligence may be artificial, but it's not "intelligence"—and it certainly isn't magic. What we call "AI" is dependent upon the construction of models from data using statistical approaches developed by flesh-and-blood humans, and it can fail just as spectacularly as it succeeds. Build a model from bad data and you get bad predictions and bad output—just ask the developers of Microsoft's Tay Twitterbot about that.

    For a much less spectacular failure, just look to our back pages. Readers who have been with us for a while, or at least since the summer of 2021, will remember that time we tried to use machine learning to do some analysis—and didn't exactly succeed. ("It turns out 'data-driven' is not just a joke or a buzzword," said Amazon Web Services Senior Product Manager Danny Smith when we checked in with him for some advice. "'Data-driven' is a reality for machine learning or data science projects!") But we learned a lot, and the biggest lesson was that machine learning succeeds only when you ask the right questions of the right data with the right tool.

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