Creating a Scalable Machine Learning Pipeline

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Project Demo

Chapter 2: Project Overview

Lecture 1: Technologies Used

Chapter 3: Data Gathering and Collection

Lecture 1: Easy Image Gathering

Chapter 4: Google AutoML Vision: Image Classification

Lecture 1: Demo

Lecture 2: About AutoML

Lecture 3: Importing Images to AutoML Vision

Lecture 4: Labeling Images

Lecture 5: Training our Model

Lecture 6: Training a Model for Cloud Use

Lecture 7: evaluate model

Lecture 8: Export Model

Chapter 5: Website Setup and Creation

Lecture 1: Setting up our Website

Lecture 2: Use Model Export

Lecture 3: Adding React Router – Navigation Logic

Lecture 4: Adding and Connecting Firebase to our Website

Chapter 6: Using Image Classification Models

Lecture 1: Image Uploader

Lecture 2: Get Images for Inference

Lecture 3: Make Predictions

Lecture 4: Fix CORS Storage

Lecture 5: Save Predictions

Lecture 6: Adding and Styling our Navigation Bar

Lecture 7: Auto Starting our Classification Inference Process

Chapter 7: Demo to Send to Friends

Lecture 1: Simple Image Classification Demo

Lecture 2: Creating a User Demo Page with File Upload

Chapter 8: Triggering the Piepline

Lecture 1: Cloud Function Setup / Overview

Lecture 2: Checking User Submitted Images

Lecture 3: Checking User Submitted Images Continued

Lecture 4: Tracking Images Uploaded by Users

Lecture 5: Create a CSV with Image File Names and Locations

Lecture 6: Update the Models Image Count

Lecture 7: Update the Models Stats and Analytics

Lecture 8: Updating the Models Labels Stats and Analytics

Chapter 9: Machine Learning Dashboard

Lecture 1: Creating our Machine Learning Dashboard

Lecture 2: Connect Dashboard to our Model's Analytics

Lecture 3: Connect Dashboard to our Label's Analytics

Lecture 4: Exporting and Saving our Data

Lecture 5: Cron Job – Schedule Model Stat Export

Lecture 6: Cron Job -Schedule Label Stat Export

Chapter 10: Human In The Loop

Lecture 1: Intro to Human In the Loop: RevaliML

Lecture 2: Create API to Review Predictions

Lecture 3: Create API to Update and Save Reviewed Predictions

Chapter 11: Deployment

Lecture 1: Deploy Classification Front-End

Lecture 2: Deploying Our Dashboard

Chapter 12: Other: Cleaning up and Finishing Project

Lecture 1: Displaying Historical Model Data

Lecture 2: Displaying Model Data in Charts

Lecture 3: Connect the Label Volume to Dashboard Donut Chart

Lecture 4: Functions Cleanup: Add to Images