Summary of "Lec 1: Introduction to the Course"

Purpose

Introductory lecture announcing a course on Distributed and Cloud Computing. Explains the core idea and motivation for cloud computing, everyday examples and benefits, and how the course is organized.

Core vision of cloud computing

Developers should be able to write, build and deploy applications without worrying about the underlying infrastructure (location, who manages servers, exact hardware specs).
Applications should be globally accessible, highly available (24/7), and scalable; the cloud provider handles maintenance, scaling and uptime.
Pay-as-you-go: consume resources only as long as you need them and pay accordingly (many providers also offer free or low-cost starter tiers).

Fundamental problems that drove cloud adoption

Storage — where to store rapidly growing amounts of data.
Computation — how to run large or long-running computations (for example, ML training) and obtain necessary CPU/GPU resources.

Common uses and examples

Cloud storage services: Google Drive, iCloud, Dropbox, Google Photos — accessible anywhere, typically with storage limits and paid tiers.
Cloud for computation: running applications, training and deploying machine-learning models, and large-scale data processing.

Business and operational models enabled by cloud

Consumers: rent infrastructure so they don’t have to buy or maintain physical servers.
Providers / surplus owners: convert underused local infrastructure into services (private → public cloud, or offer paid access to spare capacity).

Benefits emphasized

Lower upfront investment; ability to start small and scale as needed.
Focus on application logic and features instead of server maintenance.
Affordable or free options for small projects (for example, hosting a college event).

Virtualization

Virtualization is the foundational technology that made cloud computing practical and scalable.

Course structure

The course frames cloud computing as a type of distributed system and is organized roughly into the following parts:

Distributed systems fundamentals (Module 1)
- Basics of distributed computing: grid computing, cluster computing, client–server models.
Service-oriented designs (Module 2)
- Service-Oriented Architecture (SOA), web services, microservices — architecture styles that arise from distributed systems.
Virtualization (Module 3)
- Virtualization fundamentals — the core technology behind cloud platforms.
Cloud fundamentals (Modules 4 & 5)
- Definitions of cloud computing, deployment models, service delivery models (IaaS/PaaS/SaaS), billing/payment schemes, and the economics of cloud services.

Practical / lab component

Hands-on labs focused mainly on Amazon Web Services (AWS) and Microsoft Azure (instructor notes these two cover roughly 90% of the market).
Activities include programming, designing and deploying applications, and exploring platform services.

Readings and resources

Instructor recommends three standard textbooks (one per course part); PDFs are freely available online (titles not specified in the summary).

Other points

Relevance to machine learning: cloud platforms will be the primary place for building, training and deploying ML models due to their compute and storage capabilities.
Career relevance: familiarity with a major cloud platform (AWS, Azure, or Google Cloud) substantially improves job prospects.

Speakers and platforms mentioned

Speaker: the course instructor / lecturer (unnamed)
Platforms and examples referenced: Amazon AWS, Microsoft Azure, Google Cloud, IBM Cloud, Google Drive, iCloud, Dropbox, Google Photos. A website example “NITI” was mentioned in the transcript.