AWS Bedrock: An Innovative AI Tool That Empowers You to Create with Ease

  In today’s world, AI is no longer the exclusive domain of data scientists or machine learning engineers. We are entering an era where anyone can harness AI to work smarter and faster—especially professionals responsible for infrastructure management or platform development. Among the most noteworthy tools in this revolution is AWS Bedrock, a new service from Amazon Web Services that lets you access top-tier generative AI models from multiple providers without having to manage the infrastructure yourself. This article explains what AWS Bedrock is, why it’s impressive, whom it suits best, and how it can benefit platform engineers like us. Additionally, we share a brief firsthand experience to help those who might be interested in experimenting with it.   What Is AWS Bedrock and Why Is It So Exciting?   In simple terms, AWS Bedrock is a service that lets you access world-class generative AI models from several major vendors with just a click. In other words, it serves as a “one-stop shop” for high-quality AI models sourced from various providers such as Anthropic (Claude), AI21 Labs, Stability AI, Meta (Llama), and Amazon Titan (AWS’s own model). Each model offers unique strengths—some excel at summarizing information, others at translation or even image generation. Moreover, you can interact with these models through a single, unified API. The key benefits of AWS Bedrock include:   ????️ No Infrastructure Hassles: Forget about setting up servers, tweaking GPUs, or managing auto-scaling—AWS handles it all. ???? Flexibility in Model Selection: If you fancy using Claude one day and want to try Llama the next, you can switch models without any vendor lock-in. ???? Seamless Integration: It works effortlessly through APIs or SDKs, making it easy to connect with Lambda, API Gateway, or your own custom applications. ???? Security and Compliance: Benefit from the robust security standards of AWS’s trusted infrastructure.   Beyond these fundamental advantages, AWS Bedrock comes with a suite of advanced features designed to prevent misinformation and ensure the accuracy and suitability of responses:   ???? Foundation Model APIs: Access various AI models from different providers through a unified API. ????️ Custom Model Fine-Tuning: Tailor select models to fit your specific use cases. ???? Agents for Bedrock: Create agents capable of utilizing a toolchain, executing chain-of-thought reasoning, or interfacing with external data sources such as DynamoDB or other external APIs. ???? Knowledge Bases: Allow models to reference data from S3 or RDS and respond to queries based on that information. ????️ Guardrails: Set safety boundaries—for instance, preventing the discussion of sensitive subjects or the provision of incorrect data. Getting Started with AI the Easy Way Using Amazon Titan on AWS Bedrock   After discussing the theory behind AWS Bedrock, it’s time to share a hands-on experience to illustrate just how accessible this tool is. In our trial, we opted to use an AI model directly from AWS—the lightweight and cost-effective “Amazon Titan – Nova Micro.” Note that this model is currently available only in the N. Virginia region (it has yet to be launched in Thailand).   Our Use Case We set up a scenario where Nova Micro was tasked with creating a list of subtasks needed for opening a Jira ticket. The goal was to kickstart tasks quickly and minimize repetitive work. Here’s how we prepared:   Prompt Example ????✨ prompt = “Create a detailed list of subtasks needed to complete this task include testing and document. Format the response as a list where each item starts with ‘- ‘ and includes a clear and text, actionable subtask only one. The number of items may vary, but do not exceed 10. items. Task: ” Task Message message = ‘Install Jeknins on AWS ec2′   Nova Micro, our little model, works faster than expected! Just by entering the prepared prompt, the model can generate tasks completely and accurately, ready for real-world use. For example, it can instantly open a Jira task without the need to waste time thinking it through repeatedly. What I really like is the Chat mode, which is both easy and convenient — no need to write code to give it a try. And if the answer from this model isn’t quite what we’re looking for, we can easily switch to other models available in Bedrock to get the answer that best matches our needs, ready to be used for further tasks.     In real working life, we see these models as becoming key assistants in enhancing work efficiency — especially when used in combination with automation systems. And when we talk about automation, we can’t skip the topic of coding, which allows us to instruct the models to perform tasks systematically on our behalf. So today, let’s try writing a simple piece of code to call AWS Bedrock via its API.    What’s really great is that we can view the API request format directly from the AWS Console under the Bedrock section. By simply selecting the model we want to use, the system will display a ready-to-use API example right away. This makes it much easier to start coding without having to flip through multiple documentation pages or guess the parameters on our own.     We chose to use Boto3 to experiment with calling a model via AWS Bedrock, and here are the results. It must be said — it was both simple and very convenient. You could say it opens the door to using AI in daily work without complexity. AWS Bedrock truly makes AI feel much more accessible and no longer something distant or difficult to approach.   It’s just the beginning… but a crucial first step in bringing AI into your workflow.   From our trial use of AWS Bedrock with a lightweight model like Amazon Titan – Nova Micro, we’ve seen that getting started with AI through Bedrock is incredibly simple and truly accessible. Even though this was just a basic use case, it clearly helped improve both speed and efficiency in our workflow — especially when combined with code through Boto3, which allows the results to be seamlessly integrated… Continue reading AWS Bedrock: An Innovative AI Tool That Empowers You to Create with Ease

Did You Know Jenkins Has Secret Plugins? Boost Your Build Speed, Security, and Efficiency Instantly

Jenkins is a popular tool for Continuous Integration/Continuous Deployment (CI/CD) across the Software Development Lifecycle from building and testing to deploying and automation. Today, Khun Aom, Platform Services Engineer, SCB TechX reveals 5 powerful…

What is Amazon Q? Let’s AI Help a Platform Engineer Write Code—The Results Were Beyond Expectations!

generate by AI In today’s world, where AI is increasingly becoming a part of everyone’s daily life, it’s no surprise that the term “AI” is mentioned so often. These smart AIs help improve efficiency and save time on tasks. One of the most interesting tools right now is Amazon Q Developer, a Generative AI developed by AWS (Amazon Web Services), designed specifically to support developers. What is Amazon Q Developer?   Amazon Q Developer, or simply Amazon Q, is a generative AI tool developed by Amazon Web Services (AWS). It acts like an AI-powered code assistant, designed to make project development smoother and more efficient. You can easily integrate it with popular code editors like Visual Studio Code (VS Code) and JetBrains IDEs (such as IntelliJ or PyCharm) by installing a plugin in just a few simple steps. One important note: you’ll need an AWS Builder ID to log in using the Personal Profile mode, which is necessary to get Amazon Q Developer up and running on your machine. Amazon Q Developer offers several features to assist developers, such as: ???? Code Explanation: Simplifies complex code to make it easier to understand ???? Unit Test Generation: Automatically creates tests for your code ????️ Bug Detection & Fix: Identifies errors in your code and suggests fixes ???? Documentation Assistant: Helps generate or recommend documentation for your code ???? Code Refactoring: Improves your code structure without changing its logic ???? AI Chat: A chatbot that understands the context of your code for Q&A All of these features can be easily accessed by typing the / symbol in Amazon Q’s chat box or used inline within your IDE, making it quick and convenient to use while coding. After trying out Amazon Q, I found that it works like a quick and easy-to-understand knowledge source for AWS services. Whether it’s about IAM, VPC, or other services related to Platform Engineering, Amazon Q makes it all easily accessible. Additionally, Amazon Q makes coding much easier, whether it’s using the AWS SDK or writing Infrastructure as Code with Terraform. As a Platform Engineer, I was curious to see how a tool like Amazon Q, designed to assist developers, could be applied to platform-related tasks and how it could help in our work. In this post, I’ll share my hands-on experience with Amazon Q and how it performed in this context. Hands-On Experience: Amazon Q in Platform Engineering We needed to create a Proof of Concept (POC) to integrate Azure AD with Jenkins. The challenge was that we often had to delete and recreate machines multiple times, which led to wasted time reinstalling Jenkins and the required plugins each time. So, we wanted to see if Amazon Q could help generate an Ansible script to install Jenkins along with the specified plugin versions. This would allow for quick and efficient reinstallation whenever needed. How we Tested From our experience with AI Code Assistants, we knew that the best way to make tasks easier and more aligned with our needs was to write a markdown file to create a clear prompt. And this is the requirement-amazonq.md file, which specifies what we want Amazon Q to generate. 2. We used Amazon Q to generate code base on this file. Amazon Q not only generated code but also provided recommendations for running Ansible correctly. 3. We adjusted some parameters, such as the EC2 instance’s IP address, Jenkins version, and the plugin versions we wanted. After that, we tested running Ansible. How it all turned out In our first test, Ansible failed with an “Error: Unsupported parameters” which prevented us from completing the Jenkins installation (we were using the code generated by Amazon Q without making any changes yet). So we sent the error message to Amazon Q for troubleshooting and received a fix. After fixing code, we ran Ansible again but this time we encountered “unzip: command not found” error. Once again, we consulted Amazon Q, and it generated additional code to resolve this issue. After applying the changes, we ran Ansible again, and this time it worked successfully!  We were able to install Jenkins and plugins easily using Ansible script generated by Amazon Q. However, when we checked the results in Jenkins, we noticed something odd???? —why plugin version didn’t match the one we specified? Instead, it was the latest version. Although Ansible code included specifying plugin version, the result wasn’t as expected. So, we consulted Amazon Q again to review and update code. After making necessary adjustments, we ran Ansible once more and finally, everything worked perfectly as expected! ???? Amazon Q: An AI Role Beyond Just Assistance   After working with Amazon Q, it’s clear that it’s a powerful tool. It can generate initial code quickly, help adjust and refine code to match our goals, and provide valuable guidance throughout the process. Even more importantly, Amazon Q acts as a reliable partner in DevOps and platform work, helping to improve efficiency and effectiveness. The more detailed and clear we make our prompts, the better Amazon Q performs. Amazon Q isn’t just a code assistant—it’s an “AI partner” that helps us work faster and more efficiently. I hope this article helps you discover a new tool that we might see at the AWS Summit Bangkok 2025! If your organization is looking for a DevOps solution to automate processes, reduce costs, and drive sustainable growth, SCB TechX is here to help you achieve those goals. Contact us at https://forms.office.com/r/P14E9tNGFD  References: https://docs.aws.amazon.com/signin/latest/userguide/sign-in-aws_builder_id.html https://aws.amazon.com/q/developer/ https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/what-is.html

Introducing n8n: A Technical AI Workflow Tool Near Me

DevOps professionals are no strangers to writing scripts or using various tools to automate their workflows, especially in today’s AI-driven era. With this in mind, SCB TechX is excited to introduce Khun Grace, Senior Platform Services Engineer, who will be sharing insights on n8n—an automation platform…

DevOps Insider: Fix Terraform Errors Instantly with 3 Game-Changing Automation & Security Tools

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AI-generated content may be incorrect.

Infrastructure as Code (IaC) with Terraform is the core of building infrastructure, enabling automated code compilation and sharing within teams. However, to ensure the code is accurate, secure, and production-ready, Khun Tong, Senior Platform Services Engineer at SCB TechX is here to share essential tools for Automation Testing, Linting, and Security Scanning that enhance Terraform to its fullest potential. Let’s dive into the first tool …

What Are DevOps Tools? Why Does It Matter for Tech Businesses?

Software development and IT operations have undergone a paradigm shift with the emergence of DevOps—a philosophy that bridges the gap between developers and operations teams to streamline workflows, foster collaboration, and accelerate innovation. Central to the success of DevOps are DevOps tools, powerful applications that automate processes, promote efficiency, and enable teams to scale effectively.    If you’re a developer, IT professional, or part of a business looking to enhance your operations, this article will explain what DevOps tools are, their applications, and their benefits. We will also explore concepts like the DevOps process, DevSecOps, and platforms such as SCB TechX, providing actionable insights for leveraging these tools in your organization.  What is DevOps? At its core, DevOps merges two critical functions—development (Dev) and operations (Ops)—to create a seamless, collaborative ecosystem. Traditionally, these functions operated in silos, leading to inefficiencies and slower product development cycles.    DevOps integrates cultural philosophies, practices, and tools to enable organizations to deliver software faster, more reliably, and with higher quality. The approach emphasizes continuous collaboration, integration, and automation throughout the software development lifecycle.    The result? Businesses achieve a faster time-to-market, heightened operational efficiency, and enhanced customer satisfaction.  Why DevOps Matters? Modern software development drives industries like e-commerce and healthcare, powering everything from online shopping platforms to patient management tools. As customer expectations and usage grow, businesses need software that delivers seamless, reliable experiences to stay competitive in the market. DevOps helps meet these demands by streamlining development and deployment, allowing teams to quickly release updates, scale operations, and adapt to changing needs. What Is DevOps Tools? Now that we understand DevOps, let’s explore the tools that make this model a reality.   As mentioned earlier,  DevOps Tools are applications and platforms designed to automate tasks, enhance collaboration, and improve the efficiency of both development and operations teams. These tools ensure that every stage of the DevOps process—from planning to deployment—is faster, smoother, and more effective. Some key areas DevOps tools address include:  Continuous integration/deployment (CI/CD): Automating the process of merging code changes and deploying updates. Configuration management: Managing infrastructure at scale with tools like code-based provisioning. Monitoring and feedback: Tracking performance and capturing valuable insights in real-time. Security integration: Incorporating security measures directly into the DevOps workflow (DevSecOps). For instance, popular DevOps tools you may heard of or seen include Jenkins, Docker, Kubernetes, Git, Ansible, and Terraform. Each of these tools serves a unique purpose, from managing containers to automating builds and deployments.  The DevOps Process To implement DevOps, an organization must follow a series of structured steps, this is often referred to as the DevOps process. Generally, there are 6 steps in the process to ensure systematic collaboration and consistent results.   Plan: Use tools like Jira or Git to set priorities and create a roadmap for the development. Develop: Write and review code collaboratively with platforms like GitHub or Bitbucket. Build: Automate builds with tools such as Jenkins or TravisCI to streamline packaging and testing. Test: Perform rigorous testing using tools like Selenium or JUnit to ensure quality and reliability. Release: Initiate seamless deployments with Docker, Kubernetes, or OpenShift. Monitor: Continuously track application performance with tools like Nagios, Grafana, or New Relic. Each phase incorporates automation and real-time feedback, enabling teams to identify and correct issues in the early stages. Paired with practices like continuous integration and continuous delivery (CI/CD), the process ensures maximum scalability and efficiency.   So, What is DevSecOps? Security is another critical pillar added to today’s DevOps ecosystem. DevSecOps integrates security measures directly into the development workflow. In doing so, the development team can identify vulnerabilities early, reduce risks, and ensure compliance without compromising speed. Key DevSecOps practices include automating security testing, using configuration management tools to enforce security policies, and monitoring for threats in real-time. Platforms like AWS and GitLab often include DevSecOps features, giving businesses robust frameworks for secure software development. Benefits of DevOps Tools 1. Speed and Agility DevOps tools automate repetitive tasks like testing and deployment, enabling teams to focus on innovation. 2. Collaboration and Transparency By centralizing workflows on platforms like GitLab or xPlatform, DevOps tools can foster real-time collaboration. Stakeholders can monitor progress, address bottlenecks, and align more effectively.  3. Improved Quality and Reliability Tools like Jenkins and Selenium ensure continuous testing at every stage, reducing bugs and delivering stable, high-quality software.  4. Cost-Effectiveness Automation reduces the need for manual intervention, saving time and operational costs. For instance the NDID (National Digital ID) system in Thailand has integrated DevOps tools to facilitate seamless deployments while supporting thousands of simultaneous users.  5. Scaling with Confidence Organizations can scale dynamically with tools like Docker and Kubernetes, which support containerization and orchestration of applications in diverse environments.  By incorporating DevSecOps frameworks, teams also ensure their solutions remain safe and compliant, even on a large scale. Getting Started with DevOps Curious about implementing DevOps tools in your organization? Here are the general steps to get started:  Build a DevOps Culture: Foster collaboration between your development and operations teams by breaking down silos. Choose the Right Tools: Begin with popular, beginner-friendly options like Docker for containers, Terraform for configuration management, and Jenkins for automating builds.  Start Small: Pilot DevOps practices with a specific project, then scale across teams.  Measure Success: Track key metrics like deployment frequency, lead time, and failure rates to assess progress.  Focus on Automation: Maximize efficiency by automating repetitive tasks, from testing to monitoring.    Today, DevOps tools have redefined how businesses operate, giving development teams the edge they need to adapt quickly and deliver exceptional customer experiences. Whether through faster deployments, seamless scaling, or heightened security, these tools empower organizations to thrive in an increasingly competitive landscape. Transform Your Businesses with xPlatform by SCB TechX SCB TechX introduces xPlatform, an innovative solution designed to transform software development with DevOps as a Service. Offering a streamlined Self-Service Portal, xPlatform helps businesses accelerate product development, control cloud costs, and enhance security. Available Packages  Essential Package: Ideal for startups and growing businesses, offering core… Continue reading What Are DevOps Tools? Why Does It Matter for Tech Businesses?

DevOps Insider: The Monitoring Secret DevOps Experts Won’t Tell You

xPlatform DevOps Monitoring Secret for everyone

Still Struggling with Slow Monitoring Systems? It’s Time for a Change.
Today, Khun Itthikorn Anantanasarn, Platform Services Engineer at SCB TechX, is here to introduce the game-changing solution that will revolutionize your observability…

DevOps Insider : Autoscaling Supercharges Kubernetes Like a Pro, Ready for Any Load

Autoscaling in Kubernetes improves application efficiency under varying loads through three methods: Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA)…

DevOps Insider : AI x DevOps — 5 Benefits Transforming Work and Boosting Developer Happiness

In an era where DevOps and AI are revolutionizing the tech landscape, the integration of these two powerful forces is shaping the future of software development. Today, we invite you to hear from Khun Karanyot Russamee, Senior Platform Services Engineer behind the innovative xPlatform (DevOps as a Service) at SCB TechX. Discover how incorporating AI into DevOps processes is not just enhancing efficiency but also boosting developer happiness in unexpected ways.   Here are 5 key benefits of AI-driven DevOps that could transform your work: AI-Powered Incident Detection and Resolution: Real-time solutions for complex system issues. AI pinpoints anomalies, analyzes log files, and auto-generates incident reports, reducing downtime and ensuring smooth operations. Optimized CI/CD Pipelines: Save time and achieve precision with AI forecasting potential build and deploy issues, offering solutions and summarizing deployment challenges for quicker, more accurate deliveries.ฃ Smart Infrastructure Management: AI dynamically manages resources, automating system scaling based on usage and troubleshooting cloud resources without human intervention for maximum efficiency. Proactive Security Enhancements: AI combats evolving cyber threats by detecting code vulnerabilities, analyzing dependencies, and automating security testing, with machine learning to identify new attack patterns. Improved Developer Experience: From chatbots offering on-the-spot troubleshooting to AI-generated best practices, developers are empowered to focus on creating innovative solutions. At SCB TechX, we’ve harnessed our expertise in large-scale software development to create xPlatform (Platform DevOps as a Service), enhancing the developer experience and enabling businesses to deliver better solutions.   If your organization is looking for a DevOps solution to automate processes and reduce costs, SCB TechX is here to help you achieve those goals.   Contact us at contact@scbtechx.io Learn more: https://bit.ly/3KOP31b

DevOps Insider: 10 Must-Have DevSecOps Tools to Keep Your Code Safe and Secure

Integrating DevSecOps into your DevOps pipeline is key to reducing security risks without slowing down development.
In this article, Kranayot Rassamee, Senior Platform Services Engineer at SCB TechX, introduces…

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