Learn about useful AI tools in DevOps and the cloud to speed up coding, optimize cloud costs, or dramatically improve your monitoring and security systems ⬇️ Download DevOps Roadmap for free: 🚀 Get hired as a DevOps engineer for 6 months: #ai #aitools #techworldwithnana ▬ ▬▬▬▬▬ Thanks to JetBrains for making this video possible! 👏 ▬▬▬▬▬▬ ► Try TeamCity Pipelines Beta for free: ► Enjoy your CI/CD experience with self-tuning pipelines that will never break your development flow. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ▬▬▬▬ AI is the hottest topic right now. Artificial intelligence tools are being developed for all fields including various IT fields. And since you’re on the TechWorld with Nana channel, we’re going to talk about AI tools specifically for…

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33 thoughts on “Top 4 AI Tools for Engineers that are ACTUALLY useful”
  1. I think this is the first time ever disagreed with you.
    Non of the existing AI tools is even 70% mature enough for dev or prod usage. On the contrary. It even slows down the development process.
    Now pipelines is a completely other story. Because there, This is an absolute joke. AI is not Automation.
    Its not enough to have a tool that literally spams you with data it finds in stackoverflow or GitHub. You need the right context for the right time. AI tools dont know what escalation means and what kinda policies you apply during the escalation(Should i offer an radical patch/fix or something more generic).

  2. I am a huge fan of u Nana.. Thanks for ur videos.. I have 1 small request. it would be great if u make videos on kubeflow. Because thre are very few videos about kubeflow and its very hot tech right now for MLOps.. please consider . thanks allot!

  3. @TechWorldwithNana! Thank you for excellent support and services for the DevOps community! Just a request, please make two playlist in near future such as Golang for DevOps Engineer, and MLOps for DevOps Engineer. Again, thank you so much for your contributions and teching! 😊

  4. I think it's time for you to teach us MLOps. We don't know how to implement devops practices for machine learning applications. Like kubeflow we can use it as cicd for ML applications. It would be great if you create one course on MLOps. Thanks Nana

  5. you mentioned I can comment about anything, this comment is about a different video but I am going to post it anyway, you have couple of videos on Kubernetes and most of them really good but they are not for beginners, beginners I mean they are missing some of the stuff where an absolute beginners would struggle for example its missing downloading part and very basic configuration for downloading and setting up

  6. 1:43 ⚙ DevOps principles focus on automation and efficiency, and AI tools aim to handle repetitive tasks.
    2:30 🤔 Most AI tools aren't yet mature enough to be fully automated; they still require human validation.
    4:19 💻 AI code assistants help with writing and refactoring code, but their outputs often need validation.
    7:25 🔍 AI-powered monitoring tools, like DataDog's Watchdog, analyze data to proactively identify and resolve issues.
    10:09 🚀 TeamCity pipelines optimize developer productivity by providing intelligent suggestions for pipeline configuration.
    12:07 🔒 AI security tools, like Sysdig, identify and fix security vulnerabilities in complex systems.
    15:27 ☁ AI cloud optimization tools manage cloud resources efficiently and help save costs.
    17:10 🤖 Current AI tools aren't fully capable of replacing human expertise but can significantly enhance productivity in specific tasks like monitoring and security.

  7. Another problem I’m seeing is expert syndrome. Now if you don’t even understand the concept or code you can copy paste code and explanation from ChatGPT and easily show expertise.
    I don’t know where this revolution will lead us to. But if I compare to the past with RAD, rapid application development, that movement produced lot of bad applications. Let’s see 😅

  8. I find ChatGPT in general & Assistants great help. Personally I use them as a great search tool to save time. These are also great learning tools and libraries. For example:
    -How do I connect to a docker container interactively? Boom there’s my list of answers. And I do not have to look for my spiral notebook
    -Write me a dockerfile for python 3.12. In a few seconds I can get started.
    -Give me a set 5 Guids now I can quickly POC something that used to take me hours.
    I have found that these tools do not always give you correct answer or efficient code. So use them wisely.

  9. AI and writing code feel like distant topics for me. I attempted some Blazor development with DevExpress, but ChatGPT-4 struggled significantly. It often mixed code from .NET Core and .NET Framework, and even with the documentation, it frequently suggested impossible approaches. However, for code review, rapid backend prototyping, or repetitive tasks, it's excellent.

  10. while it seems that systems which constrain, analyse and debug your system seem to have some value, the current systems have a fundamental flaw. they are black box systems, which means it is entirely up to you to figure out if their hallucinations make any sense.

    because most black box ai is statistical in nature, it has a basic flaw which makes it unsuitable for a lot of workloads. it has absolutely no idea what it is talking about, and only produces statistically plausible guesses that it can't explain. even worse, it is susceptible to poisoning the output, as most providers do not bother to filter their training data for quality.

    this is why code assistants produce such bad code, which means that the people who can get the best use out of them are the ones who need them the least. in fact, due to how they are trained, and how they work, most code assistants are actually automated copyright infringement machines, which is why most open source projects are actively considering a total ban on all ai based contributions.

    for a lot of use cases, where creativity or liability are involved, black box systems are completely wrong for the job, as they cannot tell you why they suggested something. unfortunately white box systems are currently fairly primitive, rendering them unsuitable as well.

  11. Your content over my head as far as my job application but your videos help me push to get better in all realms. No sad heart I'm subscribed😂😂😂! Love ya Nana! And Thank you so much, as the effort you put in your tutorials is obvious to me.

  12. Hey Nana, would have been cool if you had covered other tools as well such as Dynatrace who pioneered AiOps and have been using machine learning for causation and prediction for over 15 years now…

  13. Hey Nana i'm a big fan of your quality content as it's helps alot and very to the point. I wanna request a vedio on AWS EKS with all needed aws services (CICD, AWS Load Balancer, VPC settings and other ) using AWS CDK and also tell us the Best Practices that we should consider. Thanks @TechWorldwithNana

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