Real-world End to End Machine Learning Ops on Google Cloud

Course Curriculum

Chapter 1: Introduction & prerequisites

Lecture 1: Hello & Introduction

Lecture 2: Github Repository for this course

Lecture 3: Discord Server for this Course

Lecture 4: Lab-Create GCP Trial Account for the course

Lecture 5: Lab-Download gcloud-cli & project configuration

Lecture 6: Course prerequisites and installations

Lecture 7: Course Overview & section walkthrough

Lecture 8: GCP Services used in the course

Chapter 2: Introduction to ML Ops

Lecture 1: Introduction To ML-Ops

Lecture 2: Key Components Principles in ML-Ops

Chapter 3: CI/CD using GCP CloudBuild,Artifact & Container Registry and CloudRun

Lecture 1: Introduction to CI/CD on GCP

Lecture 2: Introduction to GCP Container Registry and Artifact Registry

Lecture 3: Lab : Enable necessary APIs and install modules

Lecture 4: Introduction To GCP CloudRun for ML Models

Lecture 5: Overview of Steps for Flask Application – Local development

Lecture 6: Lab : Deploy Flask application using Container/Artifact Registry and CloudRun

Lecture 7: Lab: Execute PyTest locally using ChatGPT

Lecture 8: Introduction to GCP CloudBuild Service

Lecture 9: Lab : Deploy Flask application using GCP CloudBuild

Lecture 10: Lab : Setup Cloudbuild Triggers from GitHub Repo

Lecture 11: XGBoost Model Overview for Coupon Recommendations Model

Lecture 12: Lab : Deploy and implement Model Serving Flask Application and Pytest Locally

Lecture 13: Lab : Deploy ML Model to CloudRun using CloudBuild

Lecture 14: Overview of A/B Testing for ML Models using CloudRun

Lecture 15: Lab : Deploy New Version of ML Model & Update version traffic

Lecture 16: Assignment – Deploy Bike Rentals Regression Model & perform CI/CD

Chapter 4: Continuous Model Training using Cloud Composer-Airflow

Lecture 1: Overview of Data science model for Bank Marketing Campaign

Lecture 2: Introduction to Continuous Training

Lecture 3: Introduction to Airflow For Continuous Training

Lecture 4: Lab: Create Setup Airflow composer Env and Vertex AI Workbench

Lecture 5: Lab: Execute Model Training using Jupyter-Nbk on GCP

Lecture 6: Lab: Execute Airflow Dag for Machine Learning Workflow

Lecture 7: Lab : Continuous Training Pipeline in Action

Lecture 8: Implications of Failure scenarios in Continuous Training

Lecture 9: Lab: Trigger Continuous Training to capture model logs and setup alerting

Lecture 10: Overview of CI/CD for Model Training

Lecture 11: Lab : CI/CD of Model Training Code using Cloud-Build,PyTest and Github

Lecture 12: Lab :Setup CloudBuild triggers

Lecture 13: Assignment Part-1 : Setup Continuous Training for a Marketing ROI Model

Lecture 14: Assignment Part-2 : Perform CICD of the Data Science ROI Model

Lecture 15: Assignment Part-3 : Deploy Model Serving Application to GCP CloudRun

Chapter 5: Vertex AI For Data Science & Machine Learning

Lecture 1: Section Overview

Lecture 2: Introduction to Vertex AI Model Training Service

Lecture 3: Overview of Bike Share Rentals Regression Model

Lecture 4: Lab : Vertex AI Model Training using Web Console and Gcloud CLI

Lecture 5: Introduction to Vertex AI Model Registry

Lecture 6: Lab : Python SDK-Vertex AI Model Training,Model Registry and Model Deployment

Lecture 7: Lab : Execute Online & Batch prediction Service using Python SDK and jupter nbks

Lecture 8: Lab-Walkthrough Batch Prediction Output & Online Prediction jobs using Cloud Run

Lecture 9: Lab-Deploy and implement Batch Prediction Job using GCP Cloud Functions

Lecture 10: Lab : Overview of CI/CD using Vertex AI

Lecture 11: Lab :Vertex AI : CI/CD of Data science model using CloudBuild

Lecture 12: Assignment : Deploy XGBoost Model to Vertex AI

Chapter 6: Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration

Lecture 1: Introduction to Kubeflow for ML Orchestration

Lecture 2: Different Components in Kubeflow Pipelines

Lecture 3: Lab : Deploy a simple pipeline for XgBoost Model

Lecture 4: Lab : Trigger Xgboost Model using compiled json for continuous training

Lecture 5: Lab : Execute end-to-end kubeflow pipeline with model evaluation

Lecture 6: Lab Assignment: Deploy a Scikit-Learn Credit Scoring Model to Vertex Pipelines

Lecture 7: Introduction to Vertex AI Experiments

Lecture 8: Lab: Use different model hyperparameters for Xgboost with Vertex AI Experiments

Lecture 9: Lab:Train Different Data science Classification models using Experiments

Lecture 10: Assignment : Perform Experiments for Bike share Regression Model

Chapter 7: Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning

Lecture 1: Introduction to Hyperparameter Tuning on Vertex AI

Lecture 2: Lab : Implement Hyperparameter Tuning for BikeShare Regression Model

Lecture 3: Lab : Result Walkthrough & Assignment Overview

Lecture 4: Lab : Result Walkthrough & Assignment Overview

Lecture 5: Lab : Deploy Model Endpoint With Explainability Parameters

Lecture 6: Lab: Execute explainability for online predictions and Interpret results

Lecture 7: Lab: Execute explainability for online predictions and Interpret results

Lecture 8: Assignment : Perform Explainability for XgBoost Models

Lecture 9: Introduction to Model Versioning using Vertex AI Model Registry

Lecture 10: Lab : Deploy different versions of XgBoost Model to Model Registry

Lecture 11: Introduction to Vertex AI FeatureStore

Lecture 12: Lab : Create Feature store objects

Lecture 13: Lab : Ingest Data from Pandas DF into Feature Store

Lecture 14: Lab : Read Data From Vertex AI Feature Store into Pandas Df

Lecture 15: Introduction to AutoML

Lecture 16: Lab-Train and Deploy Classification Model using AutoML

Lecture 17: Lab – Train and Deploy Regression Model using AutoML

Chapter 8: Generative AI on Google Cloud

Lecture 1: Introduction to Generative AI

Lecture 2: Introduction to Large language models – PaLM 2

Lecture 3: Important keywords and concepts in LLM

Lecture 4: Lab-Generative AI Studio

Lecture 5: Lab – Execute LLM using Python & Jupyter Nbk

Lecture 6: Lab – Deploy text classification LLM Model using Python & Cloud Run

Lecture 7: Lab-Deploy Document Summarization Application using Python & Cloud Run

Lecture 8: Lab- Generate Fashion Image Descriptions using Python