Machine Learning Ops: Google Cloud – Real World Data Science

سرفصل های دوره

From Model Development to Deployment: Streamlining Machine Learning Workflows on Google Cloud

1. Introduction & prerequisites

1.1 Github Repository for the course.html

1. Hello & Introduction

2.1 Discord Server.html

2. Discord Server for this Course

3. Lab-Create GCP Trial Account for the course

4. Lab-Download gcloud-cli & project configuration

5. Course prerequisites and installations

6. Course Overview & section walkthrough

7. GCP Services used in the course

2. Introduction to ML Ops

1. Introduction To ML-Ops

2. Key Components Principles in ML-Ops

3. CICD using GCP CloudBuild,Artifact & Container Registry and CloudRun

1.1 CICD-Section-Source-Code.zip

1. Introduction to CICD on GCP

2. Introduction to GCP Container Registry and Artifact Registry

3. Lab Enable necessary APIs and install modules

4. Introduction To GCP CloudRun for ML Models

5. Overview of Steps for Flask Application - Local development

6. Lab Deploy Flask application using ContainerArtifact Registry and CloudRun

7. Lab Execute PyTest locally using ChatGPT

8. Introduction to GCP CloudBuild Service

9. Lab Deploy Flask application using GCP CloudBuild

10. Lab Setup Cloudbuild Triggers from GitHub Repo

11. XGBoost Model Overview for Coupon Recommendations Model

12. Lab Deploy and implement Model Serving Flask Application and Pytest Locally

13. Lab Deploy ML Model to CloudRun using CloudBuild

14. Overview of AB Testing for ML Models using CloudRun

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

16. Assignment - Deploy Bike Rentals Regression Model & perform CICD

4. Continuous Model Training using Cloud Composer-Airflow

1.1 continuous-training-section-source-code.zip

1. Overview of Data science model for Bank Marketing Campaign

2. Introduction to Continuous Training

3. Introduction to Airflow For Continuous Training

4. Lab Create Setup Airflow composer Env and Vertex AI Workbench

5. Lab Execute Model Training using Jupyter-Nbk on GCP

6. Lab Execute Airflow Dag for Machine Learning Workflow

7. Lab Continuous Training Pipeline in Action

8. Implications of Failure scenarios in Continuous Training

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

10. Overview of CICD for Model Training

11. Lab CICD of Model Training Code using Cloud-Build,PyTest and Github

12. Lab Setup CloudBuild triggers

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

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

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

5. Vertex AI For Data Science & Machine Learning

1. Section Overview

2. Introduction to Vertex AI Model Training Service

3. Overview of Bike Share Rentals Regression Model

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

5. Introduction to Vertex AI Model Registry

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

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

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

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

10. Lab Overview of CICD using Vertex AI

11. Lab Vertex AI CICD of Data science model using CloudBuild

12. Assignment Deploy XGBoost Model to Vertex AI

6. Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration

1. Introduction to Kubeflow for ML Orchestration

2. Different Components in Kubeflow Pipelines

3. Lab Deploy a simple pipeline for XgBoost Model

4. Lab Trigger Xgboost Model using compiled json for continuous training

5. Lab Execute end-to-end kubeflow pipeline with model evaluation

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

7. Introduction to Vertex AI Experiments

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

9. LabTrain Different Data science Classification models using Experiments

10. Assignment Perform Experiments for Bike share Regression Model

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

1. Introduction to Hyperparameter Tuning on Vertex AI

2. Lab Implement Hyperparameter Tuning for BikeShare Regression Model

3. Lab Result Walkthrough & Assignment Overview

4. Lab Result Walkthrough & Assignment Overview

5. Lab Deploy Model Endpoint With Explainability Parameters

6. Lab Execute explainability for online predictions and Interpret results

7. Lab Execute explainability for online predictions and Interpret results

8. Assignment Perform Explainability for XgBoost Models

9. Introduction to Model Versioning using Vertex AI Model Registry

10. Lab Deploy different versions of XgBoost Model to Model Registry

11. Introduction to Vertex AI FeatureStore

12. Lab Create Feature store objects

13. Lab Ingest Data from Pandas DF into Feature Store

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

15. Introduction to AutoML

16. Lab-Train and Deploy Classification Model using AutoML

17. Lab - Train and Deploy Regression Model using AutoML

8. Generative AI on Google Cloud

1.1 genai-section-source-code.zip

1. Introduction to Generative AI

2. Introduction to Large language models - PaLM 2

3. Important keywords and concepts in LLM

4. Lab-Generative AI Studio

5. Lab - Execute LLM using Python & Jupyter Nbk

6. Lab - Deploy text classification LLM Model using Python & Cloud Run

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

8. Lab- Generate Fashion Image Descriptions using Python

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تولید کننده: Udemy