# CLOUD COMPUTING WORKSHOP AND AI INTEGRATION > Source: > Published: 2026-09-15 06:57:02+00:00 What I Learned Today — Session 1 Today was the first half-day of the Cloud-Native Systems & Applied AI Integration hands-on workshop at Coimbatore Institute of Technology. The session focused on understanding cloud fundamentals and getting practical experience with AWS. Here are the major concepts I learned today. One of the simplest and most memorable explanations from today's session was the Pizza as a Service analogy. Instead of starting with complicated cloud terminology, we can understand service models by thinking about ordering pizza. On-Premises You make everything yourself. You provide: Ingredients Kitchen Oven Cooking Serving Maintenance In computing: Hardware Networking Storage Operating System Runtime Application Data Everything is managed by you. IaaS — Infrastructure as a Service Now imagine renting the kitchen and oven. The infrastructure provider manages the physical infrastructure, while you manage the operating system and applications. AWS ├── Servers ├── Networking └── Storage You ├── OS ├── Runtime ├── Application └── Data Examples include virtual machines and cloud infrastructure services. PaaS — Platform as a Service Now imagine the kitchen is already prepared. You just bring your recipe and ingredients and focus on making the pizza. Cloud Provider ├── Infrastructure ├── OS ├── Runtime └── Platform You ├── Application └── Data You don't need to worry about managing the underlying infrastructure. SaaS — Software as a Service Now imagine ordering a pizza. You simply use the finished product. You ↓ Pizza You don't manage: The kitchen Oven Ingredients Cooking Maintenance Similarly, with SaaS, you simply use the software while the provider manages the underlying system. Examples include services such as Gmail, Google Docs, and Microsoft 365. AIaaS — AI as a Service Today's discussion also introduced the idea of AI as a Service. Instead of building and maintaining the complete AI infrastructure yourself, you consume AI capabilities through a service/API. Your Application | v AI API | v AI Model / Infrastructure This makes it possible for developers to integrate AI capabilities without necessarily building and training everything from scratch. The Complete Pizza Analogy The easiest way I remember it now: ``` PIZZA AS A SERVICE ``` On-Premises ↓ Make everything yourself IaaS ↓ Rent the kitchen + infrastructure PaaS ↓ Use a ready-to-use kitchen/platform SaaS ↓ Order the finished pizza AIaaS ↓ Order AI capabilities when your application needs them This analogy made the difference between IaaS, PaaS, SaaS, and AIaaS much easier to understand. We then moved into the basics of AWS infrastructure. The important takeaway for me was that AWS isn't just a single service. It is a huge ecosystem of cloud services that can be combined to build applications. For example: AWS │ ├── Compute ├── Storage ├── Networking ├── Databases ├── Security ├── AI / ML └── Developer Tools Today, we mainly worked with S3 and CloudFront while also understanding AWS account access and identity. Another important concept I learned was the difference between the AWS Root User and an IAM User. Root User The root user is the original identity associated with an AWS account. It has extremely high-level access to the account. Conceptually: AWS Account │ └── Root User Because of its powerful permissions, the root user should not be used for everyday development activities. IAM User IAM stands for: Identity and Access Management IAM allows us to create identities with specific permissions. AWS Account │ ├── Root User │ └── IAM ├── Developer ├── Administrator └── Other users/roles This introduced me to an important cloud security principle: Give users only the permissions they actually need. This is the idea of least privilege. The next major hands-on topic was Amazon S3 — Simple Storage Service. I learned that S3 is an object storage service where data is stored as objects inside buckets. The basic structure is: AWS Account ↓ S3 ↓ Bucket ↓ Objects ↓ Files / Data We created an S3 bucket and explored its configuration. The basic workflow was: Create Bucket ↓ Configure Bucket ↓ Upload Files ↓ Configure Hosting ↓ Access Website This gave me my first practical experience with creating cloud storage rather than just reading about it. After creating the bucket, we explored different configuration options. This helped me understand that creating a bucket is only the beginning. We also need to think about: Bucket configuration Access Permissions Website hosting Objects stored inside the bucket We then uploaded files into the bucket. S3 Bucket │ ├── index.html ├── style.css ├── script.js └── assets/ This was particularly interesting because these files can form the foundation of a static website. We also learned how S3 can be configured to host static website content. The basic idea is: Browser | | Request v S3 Bucket | | index.html v Browser So a simple frontend application can be stored and served from cloud infrastructure. The final major topic of today's session was Amazon CloudFront. CloudFront is AWS's Content Delivery Network (CDN). The basic architecture we worked with was: ``` User | v CloudFront Distribution | v S3 Bucket | v Website Files ``` We learned how to create a CloudFront distribution. One of the key steps was configuring the S3 bucket as the origin. In simple terms: CloudFront | | Origin ↓ S3 Bucket The origin is essentially the location from which CloudFront obtains the content it needs to deliver. So our overall architecture became: ``` INTERNET | v USER | v ┌────────────────┐ │ CloudFront │ │ Distribution │ └───────┬────────┘ | Origin | v ┌────────────────┐ │ S3 │ │ Bucket │ └───────┬────────┘ | v Website Files ``` This was probably the most valuable part of today's hands-on work because I could see how storage and content delivery work together. My Key Takeaways from Session 1 Today's session helped me understand the cloud from three different perspectives: The biggest takeaway for me is that cloud computing isn't just about using a remote server. It's about understanding how different managed services can be combined to build, deploy, secure, and deliver applications. What's Next? This was only the first half-day of Session 1. The workshop is continuing, and I'm looking forward to getting hands-on with the remaining topics around: Docker Containers Kubernetes Generative AI AI Guardrails Agentic AI AI/ML deployment I'll continue documenting the learning journey as the workshop progresses. Session 1 — First Half: Completed