###Pendit ai checks####How AI Is Changing Cloud Computing in 2025: The Future Unfolds

Artificial Intelligence (AI) and cloud computing are two of the most transformative technologies of our time. In 2025, their convergence is reshaping how businesses operate, developers build, and IT professionals manage infrastructure. But how is AI changing cloud computing exactly? From smarter resource management to enhanced security and cost savings, AI is unlocking new possibilities in the cloud. In this guide, we’ll explore the key ways AI is revolutionizing cloud computing, why it matters, and what it means for you—whether you’re a DevOps engineer, a cloud enthusiast, or a business owner.

1. AI-Powered Resource Optimization in the Cloud

One of the biggest ways AI is changing cloud computing is through intelligent resource management. Traditional cloud setups often waste computing power due to over-provisioning or inefficient scaling. AI changes that by predicting demand and optimizing resources in real time.

• How It Works: Machine learning (ML) models analyze usage patterns—think CPU loads, storage needs, or network traffic—and adjust resources dynamically. Tools like AWS’s SageMaker or Google Cloud’s AI Platform make this seamless.
• Example: A company running an e-commerce site on Azure can use AI to scale servers during Black Friday sales, then scale down afterward, saving costs.
• Impact: Reduced waste, lower bills, and greener cloud operations.

For cloud professionals, this means mastering AI-driven tools is now a must-have skill. (Check out our AWS Certification Guide for tips on getting started!)

2. Enhanced Security with AI in Cloud Computing

Security remains a top concern in cloud computing, and AI is stepping up to the plate. By analyzing vast amounts of data, AI detects threats faster than any human could.

• How It Works: AI algorithms monitor logs, flag anomalies (e.g., unusual login attempts), and even predict breaches before they happen. Platforms like Microsoft Azure Sentinel use AI to provide real-time threat detection.
• Example: If a hacker tries to access your AWS S3 bucket, AI can lock them out and alert you instantly.
• Impact: Stronger defenses and fewer sleepless nights for IT teams.

3. AI-Driven Automation for Cloud Management

Manual cloud management is becoming a thing of the past, thanks to AI. Automation powered by AI is streamlining everything from deployments to maintenance.

• How It Works: AI integrates with DevOps tools like Kubernetes and Terraform, automating tasks like load balancing, patch updates, or container orchestration.
• Example: Google Cloud’s Anthos uses AI to manage multi-cloud environments, reducing human error and freeing up engineers for higher-value work.
• Impact: Faster deployments and lower operational costs.

For hands-on learners, our Kubernetes Basics Guide walks you through setting up an AI-optimized cluster.

4. Cost Reduction Through AI in Cloud Computing

Cost management is a perennial challenge in the cloud, but AI is flipping the script. By analyzing spending patterns, AI helps businesses get more bang for their buck.

• How It Works: AI tools like AWS Cost Explorer or CloudHealth by VMware predict future expenses and suggest optimizations—like switching to reserved instances or shutting down idle resources.
• Example: A startup using GCP might save 20% monthly by following AI-recommended instance types.
• Impact: More predictable budgets and higher ROI on cloud investments.

Curious about cloud costs? Grab our DevOps Skills Checklist .

AI isn’t just optimizing the cloud—it’s powering the apps that run on it. From chatbots to data analytics, cloud-hosted AI applications are booming.

• How It Works: Developers use cloud platforms like AWS Lambda or Azure Functions to deploy AI models (e.g., for image recognition or NLP) without managing servers.
• Example: A retailer might host an AI chatbot on Google Cloud to handle customer queries 24/7.
• Impact: Faster app development and better user experiences.

Interested in coding AI apps? Start Python with our extensive guides .

Why This Matters in 2025

The fusion of AI and cloud computing isn’t just a trend—it’s the future. As of February 2025, companies like Amazon, Microsoft, and Google are doubling down on AI-cloud integration. X posts from tech leaders show buzz around “AI-native clouds” and “self-healing infrastructure.” For CloudVedas readers, this means:

• Career Growth: Skills in AI and cloud (e.g., AWS Certified Machine Learning) are in high demand.
• Business Edge: Leveraging AI in the cloud can set your projects apart.

How to Get Started with AI in Cloud Computing

Ready to ride this wave? Here’s a quick roadmap:

1. Learn the Basics: Start with free tutorials on AWS SageMaker or Google Cloud AI (see our Free DevOps Tools post).
2. Experiment: Spin up a cheap instance on DigitalOcean or Linode to test AI workloads.
3. Certify: Boost your resume with an AI-cloud cert. Check out how I prepared for AWS, Azure, GCP, Kubernetes and many other certs.

Conclusion: AI and Cloud Computing—A Perfect Pair

So, how is AI changing cloud computing? It’s making it smarter, safer, cheaper, and more powerful. Whether you’re optimizing resources, securing data, or building next-gen apps, AI is the game-changer you can’t ignore in 2025. For CloudVedas readers, this is your chance to level up—both in skills and passive income. Add this knowledge to your toolkit, and watch your cloud projects (and earnings) soar.

What’s your take? Drop a comment below or tweet us at #CloudVedas with your AI-cloud experiences!

How to prepare for AWS Certified DevOps Engineer – Professional certification exam

Recently, I have passed the AWS Certified DevOps Engineer-Professional Certification. In this post, I will share how to prepare for this certification. Introduction AWS Certified DevOps Engineer Professional Certification is a professional certification for those who runs and manages application systems distributed on the AWS platform. This is a great certification to demonstrate your knowledge. Prerequisites Prior experience and knowledge of the foundation of AWS is recommended to start your path to professional certification. Recommended SysOps requirements include obtaining certifications such as AWS Certified SysOps administrator associate or AWS Certified Developer Associate. Additionally a thorough understanding of AWS's core services and best practices is essential to the success of this certification. Overview exam The AWS Certified DevOps Engineer Professional exam is designed to help you gain acceptance in a variety of areas including SDLC automation, configuration management, monitoring, and multiple choice and multi-purpose questions. Research sources Official AWS documentation: Official AWS documentation serves as a comprehensive resource for exam preparation. To gain in-depth knowledge of AWS services and exam-related practices, it is essential to review the AWS white paper, frequently asked questions and documentation is indispensable. Specific documentation recommended for exam preparation includes AWS Well-architected framework, AWS CloudFormation user guide, and AWS security best practices. You can also use the following methods:

Online courses and training: reputable online platforms such as Udemy , A Cloud Guru, Linux academy and Coursera offer courses focused on AWS Certified DevOps Engineer - professional exams. Several DevOps courses are available. I personally used the Udemy course "AWS Certified DevOps Engineer - Professional Certification" by Stephane Maarek .

Practice exams: practice exams play an important role in getting used to the exam format and assessing your level of preparation. The Official AWS practice test provides high quality practice tests to measure efficiency. We recommend that you book some practice tests to identify areas of improvement and improve your testing strategy. Experience To pass the AWS Certified DevOps Engineer-professional qualification exam, in addition to theoretical knowledge, practical experience in the actual AWS environment is very important. By setting up a personal AWS project and actively participating in practical exercises, you will further deepen the conceptual understanding by gaining experience applying DevOps exercises to AWS, this will improve your understanding of your business. Breakdown of test objectives

Domain 1: SDLC automation: in this domain, you need to master the principles of SDLC automation, which are focused on continuous integration and continuous deployment (CI/CD) pipelines, this will help you understand it better. Key topics: AWS CodePipeline、AWS CodeBuild、AWS CodeDeploy。 Domain 2: configuration management and infrastructure as code (IAC): knowledge of tools such as AWS CloudFormation and infrastructure, it is important to respect best practices for managing your business. Key topics: AWS CloudFormation、AWS CLI、AWS SDK。 Domain 3: monitoring and logging: in this domain, it is important to understand AWS monitoring and logging services, effective installation and configuration of logging and monitoring solutions is a key area of focus. Key topics: Amazon CloudWatch, AWS CloudTrail, Amazon CloudWatch Logs. Domain 4: automating AWS implementation policies and standards automating security best practices and policies and standards is the key to this domain. Key topics: AWS Identity and access management (IAM)、AWS configuration Domain 5: events and feedback: this domain focuses on the event feedback strategy and the efficient use of AWS CloudWatch and AWS CloudTrail. Key topics: AWS CloudTrail, AWS Config, AWS CloudWatch event.

Exam day tips On exam day, it is important to effectively manage your time and solve the problem with a calm and focused mindset. Improve exam performance with strategies such as carefully reading questions, eliminating wrong choices, and reviewing difficult questions. Stay focused and calm during the exam and don't spend too much time on any specific questions, just mark them for later review. There are many easy questions, tackle them first. Conclusion Obtaining AWS Certified DevOps Engineer - Professional Certification is an important milestone on the DevOps path and professional development and success of resources in the dynamic DevOps field requires constant learning, manual practice and the latest development of AWS services to maintain control over information.

Solved : Vagrant failed to initialize at a very early stage powershell error

If you are using Vagrant on Ubuntu and encounter any errors related to the Powershell executable, there is no need to panic. This is a very common problem and easy to solve. Here is a simple guide to resolve the “No Powershell executable found in available PATH” error.

Problem:

When you run vagrant status, you may see something like this:

bash
root@cloudvedas:~# vagrant status

Vagrant failed to initialize at a very early stage:
Failed to locate the powershell executable on the available PATH. Please
ensure powershell is installed and available on the local PATH, then
run the command again.

Basically, Vagrant can't find Powershell in your system PATH.

Solution: Add Powershell to PATH

To fix this issue, you need to add the Powershell executable to your system's PATH. Here's how to do this in Ubuntu:

1. Open Terminal: First of all, open Terminal.

2. Update your PATH: Run the following command to add the Powershell executable to your PATH.

   bash
   PATH='${PATH}:/mnt/c/Windows/System32/WindowsPowerShell/v1.0'
   

   This command simply adds the Powershell directory to your existing PATH variable.

3. Check your PATH: Verify that the PATH update is working by running:

   bash
   echo $PATH
   

   You should see /mnt/c/Windows/System32/WindowsPowerShell/v1.0 in the output.

4. Run Vagrant again: Try running the Vagrant command again.

   bash
   vagrant status
   

Summary

Adding the Powershell path to your system's PATH variable will fix the error, allowing you to use Vagrant without any issues. This way, Vagrant can find the Powershell executable it needs to function properly.

If the issue persists, check the paths you added and make sure that Powershell is installed correctly on your system.

Python: difference between list and tuple with examples

Lists and tuples both are used to store data in a sequence in Python. Lists are enclosed in square brackets [ ], whereas tuples are enclosed in parentheses ( ).

Here is an example of creating a list in Python:

cv_list = [1, 2, 3, 4, 5]


And here is an example of creating a tuple in Python:


cv_tuple = (1, 2, 3, 4, 5)


To access elements in a list or tuple, you use indexing and slicing. The first element in a list or tuple has an index of 0.

Here is an example of indexing in a list:


print(cv_list[0]) # Output: 1


And here is an example of indexing in a tuple:


print(cv_tuple[0]) # Output: 1


You can also slice a list or tuple to access a range of elements. To slice a list or tuple, you specify the start index and end index separated by a colon :.

Here is an example of slicing a list:


print(cv_list[1:3]) # Output: [2, 3]


And here is an example of slicing a tuple:

print(cv_tuple[1:3]) # Output: (2, 3)


In summary, lists and tuples are both useful data structures in Python, but they have some fundamental differences. Lists are mutable, while tuples are immutable. Lists are used when you need to add, remove, or change elements, while tuples are used when you need to store a collection of elements that won't change. Understanding the differences between these two data structures will help you make the right choice for your program.

How to prepare for Certified Kubernetes Administrator exam

Hello everyone I have recently cleared the Certified Kubernetes Administrator (CKA) exam by Cloud Native Computing Foundation (CNCF), in collaboration with The Linux Foundation.

In this blog I will share the strategy I used to prepare for the exam and the important tips you should remember while you are actually giving the exam.

My CKA exam was on Kubernetes 1.22 version which is the latest exam version as of 21-Oct-21. You can see broad level domains tested in this exam here .