DEV Community: PRAKATHI VM

cloud workshop day -1

PRAKATHI VM — Wed, 16 Sep 2026 03:43:31 +0000

🚀 Prakathi Meets Proton: Exploring AWS Proton

Introduction

As part of my AWS Cloud learning journey, I explored AWS Proton, a service designed to simplify the deployment and management of modern applications.

What is AWS Proton?

AWS Proton is a fully managed application deployment service that helps development teams deploy and manage containerized and serverless applications in a consistent way.

Why was AWS Proton created?

In larger projects, developers often need to configure infrastructure, deployment pipelines, networking, and other resources repeatedly. This can make deployments complex and inconsistent.

AWS Proton helps solve this by providing standardized infrastructure and deployment templates, allowing developers to focus more on their applications.

⚙️ How AWS Proton Works

AWS Proton uses templates to define how an application and its infrastructure should be deployed.

A simple flow is:

Developer
    ↓
AWS Proton
    ↓
Service Template
    ↓
Environment
    ↓
Application Deployment
    ↓
AWS Infrastructure

Administrators can create templates containing the required infrastructure configuration. Developers can then use these templates to deploy their applications without manually configuring every infrastructure component.

🔑 Key Features

1. 📋 Templates

AWS Proton uses environment and service templates to standardize infrastructure and application deployments.

This helps different teams follow the same deployment structure.

2. 🚀 Automated Deployments

Proton can automate application deployments and updates, reducing repetitive manual configuration.

3. 🌐 Environment Management

AWS Proton helps manage application environments such as development, testing, and production using standardized configurations.

4. 🔄 Version Management

Templates can have different versions, allowing teams to manage infrastructure changes in a controlled manner.

🎓 College / Student Use Case

AWS Proton can be useful for a college department where multiple student teams develop different applications.

For example, suppose our department has several AI/ML projects:

                 AWS Proton
                     ↓
          Standard Deployment Template
                     ↓
       ┌─────────────┼─────────────┐
       ↓             ↓             ↓
   Project 1      Project 2      Project 3
    AIML App       Web App        API

Instead of configuring the infrastructure separately for every project, a common Proton template could be used.

This provides a consistent deployment process for student projects.

💻 Simple Example

Suppose a student team creates a containerized ML prediction API.

Instead of manually setting up the deployment infrastructure, the team can use an AWS Proton service template.

ML API
  ↓
Container
  ↓
AWS Proton
  ↓
Proton Service Template
  ↓
AWS Deployment Environment
  ↓
Running Application

The template defines the infrastructure and deployment configuration required by the application.

✅ Advantages

Standardization: Provides a consistent deployment approach.
Automation: Reduces repetitive infrastructure configuration.
Scalability: Helps manage multiple application environments.
Developer Productivity: Developers can focus on application code rather than repeatedly setting up infrastructure.
Centralized Management: Teams can manage standardized templates and their versions.

⚠️ Limitations & Things to Consider

💰 Cost

AWS Proton itself does not have a separate service charge, but the AWS resources used by the deployed applications and infrastructure are billed separately.

🧩 Complexity

Understanding Proton templates and the underlying AWS services can require some cloud knowledge.

📈 Scalability

Proton is useful when managing multiple standardized applications and environments, but simpler projects may not need it.

🔐 Security

Proper IAM permissions and secure configuration of the underlying AWS resources are important when deploying applications.

🎯 Conclusion

AWS Proton provides a structured way to standardize, automate, and manage application deployments.

For student projects and college environments, it can help teams follow a common deployment process while reducing repetitive infrastructure configuration.

As an AI/ML student, learning AWS Proton helped me understand how application development and cloud infrastructure can be managed together.

Prakathi meets Proton — and this is one more step in my AWS Cloud journey! ☁️🚀

cloud workshop - session 2

PRAKATHI VM — Tue, 15 Sep 2026 11:08:29 +0000

Got it 👍 Only from the 2 images you just sent, without adding the earlier notes:

☁️ AWS Cloud Workshop – My Learning Experience

Today’s AWS Cloud Workshop gave me an opportunity to explore practical concepts in cloud computing and AWS services.

We worked on the Cloud Resume Challenge, where AWS was used to build and deploy a cloud-based project. I also learned how to work with Docker Hub and AWS EC2, including configuring permissions for EC2 to access Amazon S3.

One of the hands-on tasks involved establishing an EC2–S3 connection and downloading a dataset from S3 using the AWS CLI.

The workshop also introduced me to Serverless Computing and Event Triggers. I learned about AWS Lambda, a serverless compute service where we provide the code and AWS manages the underlying servers.

A basic event-driven flow I learned was:

S3 → Event Trigger → Lambda → Process Data

I also explored concepts related to Amazon CloudWatch for monitoring and API Gateway, which provides APIs for applications to interact with backend services.

Overall, the workshop helped me understand how EC2, S3, Lambda, CloudWatch, API Gateway, and Docker can work together to build cloud-based applications.

It was a great hands-on learning experience and helped me gain a better understanding of AWS, serverless architecture, and cloud deployment. 🚀☁️

#AWS #CloudComputing #EC2 #S3 #Lambda #CloudWatch #APIGateway #Docker #Serverless #AWSWorkshop

cloud workshop day 1 - session 1

PRAKATHI VM — Tue, 15 Sep 2026 06:52:21 +0000

☁️ My AWS Cloud Workshop Journey: From Cloud Basics to Deploying My Portfolio

Cloud computing has become an important part of modern software development, and as an AI/ML student, I wanted to gain practical exposure to how cloud technologies are used in real-world applications.

Recently, I attended an AWS Cloud Workshop that gave me both theoretical knowledge and hands-on experience with AWS services.

☁️ Understanding Cloud Computing

The workshop began with the fundamentals of cloud computing and how organizations can use cloud infrastructure instead of maintaining physical servers.

I learned about different cloud service models:

IaaS – Infrastructure as a Service
PaaS – Platform as a Service
CaaS – Containers as a Service
FaaS – Function as a Service
SaaS – Software as a Service
AIaaS – AI as a Service

Understanding these models helped me see how cloud platforms provide different levels of infrastructure and services depending on application requirements.

🤖 AI as a Service

As an AI/ML student, AIaaS was particularly interesting to me.

AIaaS allows developers and organizations to use AI capabilities through cloud services without having to build and maintain the entire AI infrastructure themselves.

This can be useful for applications involving machine learning, generative AI, computer vision, natural language processing, and other AI workloads.

🔐 Learning About AWS IAM

Another important concept I learned was AWS IAM (Identity and Access Management).

IAM helps control:

Who can access AWS resources
What resources they can access
What actions they are allowed to perform

I also understood the importance of following security practices such as protecting root-user credentials and giving users only the permissions they actually need.

Exploring Amazon S3

One of the major AWS services I explored was Amazon S3 (Simple Storage Service).

S3 is an object storage service that can be used to store:

Images
Videos
Documents
Datasets
Backups
Website files

I learned about S3 buckets, objects, Block Public Access, versioning, tags, and presigned URLs.

🔄 S3 Versioning

S3 Versioning allows multiple versions of an object to be maintained. This can be useful when files are accidentally overwritten or deleted.

🔗 Presigned URLs

I also learned about presigned URLs, which can provide temporary access to private S3 objects without making the entire bucket publicly accessible.

🚀 My Hands-on Project – Portfolio Deployment

The most exciting part of the workshop was applying what I learned.

I created my personal portfolio website and deployed it using AWS.

First, I used Amazon S3 to store and host my portfolio files. Then, I integrated Amazon CloudFront to deliver the website through a CDN.

🌐 Deployment Architecture

Personal Portfolio
       ↓
   Amazon S3
       ↓
 Amazon CloudFront
       ↓
     Users

This hands-on implementation helped me understand the complete process of taking a website from development to cloud deployment.

Instead of running the portfolio only on my local machine, I was able to make it accessible through AWS infrastructure.

💡 Key Takeaways

This workshop helped me gain both conceptual and practical knowledge of:

☁️ Cloud Computing
AWS fundamentals
IaaS, PaaS, CaaS, FaaS & SaaS
AI as a Service
🔐 IAM and cloud security
🪣 Amazon S3
🔄 S3 Versioning
🔗 Presigned URLs
🌐 Amazon CloudFront
🚀 Website deployment on AWS

🎯 Final Thoughts

The workshop was a valuable learning experience because it wasn't limited to just understanding AWS concepts. I was able to build something and deploy it using AWS services.

Deploying my own portfolio with Amazon S3 and CloudFront gave me a better understanding of how cloud technologies can be used in real-world web applications.

This experience has motivated me to explore more AWS services and eventually integrate cloud computing with my AI/ML projects.

This is just the beginning of my AWS journey. ☁️🚀