In Class Tutorials / Demos for GCP (and AWS)

Tutorials are grouped by the lecture they accompany.


Lecture 1: Virtual Environments and Virtual Machines

Ed lesson

Create Simple Translate App - No Docker, No UV

Create Simple Translate App with UV (GCP) / Pipenv (AWS), No Docker


Lecture 2: Containers I

Ed lesson

Create Simple Translate App with Docker, Push Image to Docker Hub

Run App in VM using Docker


Lecture 3: Containers II

Ed lesson

Mega Pipeline App


Lecture 4: Containers III

Ed lesson

Docker

Mega Pipeline App with Flexible Workflow

Docker Compose: Port Toy App

  • Description: Container networks and ports. Two containers, one published port, one message.
  • GCP GitHub URL: Port Toy App

Lecture 5: Introduction to RAG

Ed lesson

LLMs

LLM-RAG

  • Description: Building a RAG System with Vector DB and LLM
  • GCP GitHub URL: LLM-1

Lecture 6: LLM: Agents I

Ed lesson

Tutorial 1: Cheese Quiz

  • Description: LLM Agents notebook (Cheese Quiz)

Tutorial 2: Travel Planner

  • Description: LLM Agents notebook (Travel Planner)

Tutorial 3: Cheese Newsletter - Harness

  • Description: LLM Agents notebook (Cheese Newsletter - Harness)
    • Cheese Newsletter - Harness: Colab

Lecture 7: LLM: Agents II (practical use)

Ed lesson

LLM-Agents


Lecture 8: LLM: Finetuning

Ed lesson

LLM-Fine Tuning

LLM Finetuning Hooks A

LLM Finetuning Hooks B


Lecture 9: Connecting to the Cloud: Networking, Authentication & Storage

Ed lesson


Lecture 10: Automating Software Development: CI, unit testing

Ed lesson

Testing with Pytest

  • Description: Unit, integration, system and acceptance tests with pytest
  • GCP GitHub URL: Testing with Pytest

Automating with Pre-commit Hooks

  • Description: Local git hooks that run linters, formatters and tests before each commit
  • GCP GitHub URL: Pre-commit Hooks

Lecture 11: Data Labeling, Versioning and Monitoring

Ed lesson

Label Studio

Data Versioning

  • Description: Learn about versioning practices in development. Particularly, how to use DVC for data versioning.
  • GCP GitHub URL: DVC

Lecture 12: App Design

Ed lesson


Lecture 13: Advanced Training Workflows: experiment tracking (W&B), multi-GPU, serverless training (Vertex AI)

Ed lesson

Experiment Tracking

Advanced Workflow: Serverless Model Training with Vertex AI


Lecture 14: Serverless Deployment: Cloud Functions, Cloud Run

Ed lesson

Cloud Function and Cloud Run


Lecture 15: ML Workflows with Vertex AI

Ed lesson

Model Deployment using Vertex AI

ML Workflow

  • Description: Vertex AI ML Workflow for pipeline. Data Processing, data collection, model training, model deployment.
  • GCP GitHub URL: ML Workflow
  • AWS GitHub URL: ML Workflow - AWS

Lecture 16: System Tradeoffs: Performance, Cost, and Design Choices

Ed lesson


Lecture 17: Evaluating and Monitoring ML Systems in Production

Ed lesson


Lecture 18: APIs + Frontend

Ed lesson

Frontend and FastAPI

  • Description: Simple Frontend with FastAPI
  • GCP GitHub URL: App v1

Backend and FastAPI

Frontend Simple

Frontend React


Lecture 19: Scaling: Kubernetes (local)

Ed lesson

Scaling: Kubernetes (local)

  • Description:
  • GCP GitHub URL:
Tutorial for Scaling: Kubernetes (local) coming soon

Lecture 20: Deployment: Pulumi

Ed lesson

Deployment of the full app to GCP (Manual)

Deployment to GCP with Pulumi


Lecture 21: Scaling: Kubernetes

Ed lesson

Deployment with Scaling using Kubernetes (cloud)


Lecture 22: Automating Software Development: CI/CD with GitHub Actions and other tools II

Ed lesson

Remote CI/CD with GitHub Actions


Lecture 23: Final Deployment and Review

Ed lesson

Continuous Integration and Continuous Deployment