QTM 350 - Data Science Computing
Lecture 14 - Introduction to Cloud Computing
Local LLMs and AI agents
- We saw many cool things last class!
- There are many pros of using local LLMs:
- Privacy, security, control, and customisation
- Ollama is a great software for that!
- It lets you download and run hundreds of LLMs locally
- It’s free and open source, and easy to customise with
Modelfiles
- We also saw how to use LM Studio and Hugging Face
- You have more than two million models to choose from!
Local LLMs and AI agents
- We learnt about LLM APIs and how to use them via OpenRouter
- There are many free models available, just search for “free” in the search bar
- We also saw how to use Roo Code and integrate APIs in VS Code
- You can use any API with a friendly interface
- You can fully automate your workflow with agents
- Finally, we learnt how to use Web-UI
- Web-UI makes websites accessible for AI agents, and it can perform pretty much any task on the internet
- Have a look at its parent project too: Browser Use
- You can use it in Python and integrate it in your scripts!
What is cloud computing?
- Cloud computing is the on-demand delivery of computing resources through a cloud services platform via the internet with pay-as-you-go pricing
- Cloud computing has increased in popularity in recent years because it offers many advantages
- Scalability, flexibility, and cost-efficiency
- There are three main types of cloud computing services: IaaS, PaaS, and SaaS
- The most popular cloud computing platforms are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), the “Big Three”
- They are so widely used that Amazon’s AWS is the most profitable division of the company
- Although it is only 15% of their total sales, it accounts for more than 50% of their profits (~$28 billion)
Source: GeekWire (2025)
Case study: Animoto
- Animoto: Lets users create videos from their own photos/music
- Auto-edits photos and videos and aligns them with the music, so it “looks good”
- Built using Amazon EC2 (Elastic Compute Cloud) + S3 (Simple Storage Service) + SQS (Simple Queue Service)
- Released a Facebook app in mid-April 2008
- More than 750,000 people signed up within 3 days
- EC2 usage went from 50 machines to 3,500 (x70 scalability!)
- No way they could have done this with traditional infrastructure!
Case study: The Washington Post
- Hillary Clinton’s official White House schedule released to the public
- 17,481 pages of non-searchable, low-quality PDF
- Very interesting to journalists, but would have required hundreds of man-hours to evaluate
- Peter Harkins, Senior Engineer at The Washington Post: “Can we make that data available more quickly, ideally within the same news cycle?”
- Tested various Optical Character Recognition (OCR) programs; estimated required speed
- Launched 200 EC2 instances; project was completed within nine hours using 1,407 hours of VM time ($144.62)
- Results available on the web only 26 hours after the release
Source: The Washington Post
Cloud computing services
SaaS
Cloud computing services
PaaS
Cloud computing services
FaaS
Cloud computing services
IaaS
What is middleware?
- Middleware is software that connects two separate applications
- This is software we rarely see, but which is essential for the functioning of the internet
- For instance, software that handles authentication, authorisation, and encryption, drivers that connect to databases, and software that handles messaging
- Amazon SQS, Apache Kafka, and RabbitMQ are examples of middleware
What is virtualisation?
- Suppose Alice has a machine with 4 CPUs and 8 GB of memory, and three customers:
- Bob wants a machine with 1 CPU and 3 GB of memory
- Charlie wants 2 CPUs and 1 GB of memory
- Daniel wants 1 CPU and 4 GB of memory
- What should Alice do?
- Alice can sell each customer a virtual machine (VM) with the requested resources
- From each customer’s perspective, it appears as if they had a physical machine all by themselves (isolation)
More about virtualisation
The Big Three ☁️
Amazon Web Services (AWS)
- Amazon Web Services is a collection of cloud-based services
- It’s a very large platform
- Let me say it again: a VERY LARGE ONE 😂
- They offer a wide range of services:
- Compute, storage, databases, analytics, machine learning, AI, IoT, security, and more
- Many companies use AWS, including Netflix, Airbnb, and NASA
- The most widely used services are EC2, S3, and RDS, for computing, storage, and databases, respectively
- Let’s look at them in more detail
Amazon EC2 - Elastic Compute Cloud
- Amazon EC2 is a web service that provides resizable compute capacity in the cloud
- It’s designed to make web-scale cloud computing easier for developers
- You can launch instances with a variety of operating systems (mainly Linux)
- Which means that, for the most part, you can use Bash and run any software you want from the command line
- You can also use a GUI, but it is not necessary
- You can use EC2 to host a website, run a database, or run a machine learning model
- However, please be mindful of the costs!
- You pay for the instances you use, and the costs can add up quickly
- Per-second (or per-hour) billing
- Data transfer is not included!
- Persistent storage is not included!
- Scaling is not included!
EC2 auto scaling
- Scaling is the ability to increase or decrease the compute capacity of your application
- Scale your application manually, on a scheduled basis or on demand
- This is useful when you have fluctuating workloads, such as a website that gets more traffic during the day, or a machine learning model that gets more requests at certain times
- You can set up auto scaling groups to automatically scale your application
- You can also set up load balancers to distribute traffic across multiple instances
Source: AWS
ELB - Elastic Load Balancer
- ELB is a service that automatically distributes incoming application traffic across multiple targets
- It can handle the varying load of your application traffic in a single availability zone or across multiple availability zones
- ELB is a key component of auto scaling
- As with EC2, you pay for the data transfer and the number of requests
- And it can be quite expensive too
- It can detect unhealthy instances and reroute traffic to healthy instances
- So your application is always available!
Cloud storage
Amazon S3 - Simple Storage Service
- Amazon has three main storage services: S3 (Simple Storage Service), EBS (Elastic Block Store), and EFS (Elastic File System)
- S3 is a web service that provides scalable object storage for data backup, archival, and analytics
- You can store data in buckets, which are like folders
- Data are distributed across a minimum of three availability zones
- 99.999999999% durability (eleven nines!)
- You can set up lifecycle policies to automatically move data to cheaper storage classes
- Standard, Intelligent-Tiering, Glacier, Glacier Deep Archive
- You can also set up versioning to keep multiple versions of an object (works like
git)
Cloud storage
Amazon EBS - Elastic Block Store
- EBS is a web service that provides block storage volumes for use with EC2 instances
- The difference between S3 and EBS is that EBS is block storage, while S3 is object storage
- Block storage is like a hard drive, while object storage is like a file system
- Why would you use it? Mainly for SQL files
- Low-latency, high-performance storage for frequent access
- You can attach EBS volumes to your EC2 instances
- You can also take snapshots of your volumes for backup (like
gitagain!) - Up to 16 TB per volume
Amazon EFS - Elastic File System
- EFS is a web service that provides scalable file storage for use with EC2 instances
- EFS is mainly used for shared file storage
- Multiple EC2 instances can access the same file system
- Useful for big data and analytics
- Also for content management and web serving
- You can create file systems and mount them to your EC2 instances
- As with S3 and EBS, you pay for the storage you use
Database services
Amazon RDS - Relational Database Service
- Amazon RDS is a web service that makes it easy to set up, operate, and scale a relational database
- You can choose from six popular database engines:
- Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server
- You can also use Aurora Serverless to automatically scale your database
- It’s a managed service, so you don’t have to worry about backups, patches, or updates
- It works well with another AWS service called Athena, which allows you to query data in S3 using SQL
- You can also use Redshift for data warehousing and business analytics, and DynamoDB for NoSQL databases
Amazon SageMaker
- Finally, let’s talk about Amazon SageMaker
- SageMaker is a fully managed service that allows you to build, train, and deploy machine learning models at scale
- It has several pre-configured models, such as TensorFlow, PyTorch, and Scikit-learn
- You can also bring your own models too, and use Jupyter notebooks to train them
- Lots of other models available in the AWS Marketplace, and you can also use SageMaker Studio to manage your projects
- Machine Learning: application services
Creating an AWS account
You can do this later if you want
- To create an AWS account, go to https://aws.amazon.com/free/ and click on Create a Free Account
- Then, add your email and choose an account name (any name will do)
- Enter your account information, and then choose Verify email address
- You will receive an email with a verification link, add the code from the email
- Follow the instructions to set up your account
- Enter your billing information, yes, you need to 😒
- Enter the code displayed in the CAPTCHA, and then submit
- Choose Complete sign up
- And you’re done 🎉
Managing an AWS account
Audio transcription at scale
- Imagine that you have a task similar to that of the Washington Post journalists
- You found a series of audio files that you need to transcribe, and you have to do it quickly
- So let’s create an S3 bucket (folder), upload an audio file, and transcribe it
- The file we will use is available here: transcribe-sample.mp3
- Select AWS Management Console to open the console, then search for
S3in the search bar - Click on Create bucket, give it a name, accept the permissions, and click Create bucket
Audio transcription at scale
S3 bucket
Then click on the bucket and upload the file
Create transcription job
- Now let’s go to Amazon Transcribe
- Just search for
Transcribein the search bar - Click on Create job
- Give it a name, select the language, and choose the S3 bucket where the file is located
- Click Create
Transcription job
Transcription job
What we learned today
- Why cloud computing is important and how it can help your business
- The Big Three cloud computing platforms: AWS, Azure, and GCP
- The main types of cloud computing services: IaaS, PaaS, SaaS, FaaS
- The most popular AWS services: EC2, S3, RDS, SageMaker
- How to create an AWS account and set up a billing alarm
- How to create an S3 bucket, upload an audio file, and transcribe it with Amazon Transcribe
- And that’s just the beginning!
- In the next sessions, we will learn more about AWS services, and how to use them to build and deploy applications
- Remember to close your EC2 instances and S3 buckets when you’re done!
