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

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

位教师：Packt - Course Instructors

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8个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

8个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Set up end-to-end ML workflows using Google Cloud services like Vertex AI and Cloud Composer.
Implement CI/CD pipelines for machine learning models to automate deployment and training.
Leverage Kubeflow for orchestrating and automating machine learning workflows.
Apply model explainability techniques and hyperparameter tuning for improved model performance.

您将获得的技能

要了解的详细信息

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作业

9 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有8个模块

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this hands-on course, you will master managing the entire lifecycle of machine learning models on Google Cloud Platform (GCP). Starting with setting up your environment, you’ll learn about CI/CD pipelines, model deployment using Cloud Run, and automating workflows with tools like Airflow and Kubeflow. Key topics like continuous training, version control, hyperparameter tuning, and model explainability will also be covered. Using Vertex AI and GCP services, you’ll gain real-world experience with model training, batch prediction, and scaling. This course is designed for machine learning engineers, data scientists, and software engineers. Basic knowledge of machine learning concepts and Google Cloud Platform is recommended. By the end, you’ll be able to deploy, monitor, and scale ML models on GCP, making you proficient in ML Ops practices and cloud-based model management.

In this module, we will introduce the course objectives and setup procedures, including creating a GCP trial account, configuring the gcloud CLI, and reviewing the course structure. You'll also learn about the key Google Cloud services that will be used throughout the course.

涵盖的内容

7个视频1篇阅读材料

7个视频总计12分钟

Hello & Introduction 1分钟
Discord Server for this Course 1分钟
Lab-Create GCP Trial Account for the course 1分钟
Lab-Download gcloud-cli & project configuration 3分钟
Course prerequisites and installations 2分钟
Course Overview & section walkthrough 2分钟
GCP Services used in the course 1分钟

1篇阅读材料总计10分钟

Full Course Resources 10分钟

In this module, we will introduce you to the fundamental concepts of ML Ops, covering its principles, components, and the critical role it plays in managing the lifecycle of machine learning models.

涵盖的内容

2个视频1个作业

In this module, we will dive into the CI/CD processes on Google Cloud, teaching you how to automate build, testing, and deployment workflows for machine learning models and applications. You’ll gain hands-on experience deploying Flask applications and setting up automated deployment pipelines.

涵盖的内容

16个视频1个作业

16个视频总计70分钟

Introduction to CI/CD on GCP 3分钟
Introduction to GCP Container Registry and Artifact Registry 2分钟
Lab : Enable necessary APIs and install modules 3分钟
Introduction To GCP CloudRun for ML Models 2分钟
Overview of Steps for Flask Application - Local development 1分钟
Lab : Deploy Flask application using Container/Artifact Registry and CloudRun 13分钟
Lab: Execute PyTest locally using ChatGPT 5分钟
Introduction to GCP CloudBuild Service 2分钟
Lab : Deploy Flask application using GCP CloudBuild 5分钟
Lab : Setup Cloudbuild Triggers from GitHub Repo 5分钟
XGBoost Model Overview for Coupon Recommendations Model 5分钟
Lab : Deploy and implement Model Serving Flask Application and Pytest Locally 8分钟
Lab : Deploy ML Model to CloudRun using CloudBuild 5分钟
Overview of A/B Testing for ML Models using CloudRun 3分钟
Lab : Deploy New Version of ML Model & Update version traffic 6分钟
Assignment - Deploy Bike Rentals Regression Model & perform CI/CD 3分钟

1个作业总计15分钟

CI/CD using GCP CloudBuild, Artifact & Container Registry and CloudRun - Assessment 15分钟

In this module, we will explore continuous model training with Cloud Composer and Airflow, focusing on automating the retraining process and handling failure scenarios in machine learning workflows. You’ll also learn how to implement logging, alerting, and CI/CD for model training pipelines.

涵盖的内容

15个视频1个作业

15个视频总计59分钟

Overview of Data science model for Bank Marketing Campaign 3分钟
Introduction to Continuous Training 3分钟
Introduction to Airflow For Continuous Training 2分钟
Lab: Create Setup Airflow composer Env and Vertex AI Workbench 5分钟
Lab: Execute Model Training using Jupyter-Nbk on GCP 6分钟
Lab: Execute Airflow Dag for Machine Learning Workflow 8分钟
Lab : Continuous Training Pipeline in Action 2分钟
Implications of Failure scenarios in Continuous Training 2分钟
Lab: Trigger Continuous Training to capture model logs and setup alerting 7分钟
Overview of CI/CD for Model Training 2分钟
Lab : CI/CD of Model Training Code using Cloud-Build,PyTest and Github 9分钟
Lab : Setup CloudBuild triggers 3分钟
Assignment Part-1 : Setup Continuous Training for a Marketing ROI Model 2分钟
Assignment Part-2 : Perform CICD of the Data Science ROI Model 1分钟
Assignment Part-3 : Deploy Model Serving Application to GCP CloudRun 2分钟

1个作业总计15分钟

Continuous Model Training using Cloud Composer-Airflow - Assessment 15分钟

In this module, we will delve into Vertex AI, Google Cloud’s end-to-end platform for machine learning. You’ll learn how to train, deploy, and manage models with Vertex AI, while also exploring automated prediction services and CI/CD integration.

涵盖的内容

12个视频1个作业

12个视频总计66分钟

Section Overview 1分钟
Introduction to Vertex AI Model Training Service 3分钟
Overview of Bike Share Rentals Regression Model 2分钟
Lab : Vertex AI Model Training using Web Console and Gcloud CLI 12分钟
Introduction to Vertex AI Model Registry 2分钟
Lab : Python SDK-Vertex AI Model Training,Model Registry and Model Deployment 10分钟
Lab : Execute Online & Batch prediction Service using Python SDK and jupyter nbks 4分钟
Lab-Walkthrough Batch Prediction Output & Online Prediction jobs using Cloud Run 10分钟
Lab-Deploy and implement Batch Prediction Job using GCP Cloud Functions 8分钟
Lab : Overview of CI/CD using Vertex AI 3分钟
Lab : Vertex AI : CI/CD of Data science model using CloudBuild 9分钟
Assignment : Deploy XGBoost Model to Vertex AI 3分钟

1个作业总计15分钟

Vertex AI For Data Science & Machine Learning - Assessment 15分钟

In this module, we will cover Kubeflow for workflow orchestration, showing how to deploy and manage machine learning pipelines using Vertex AI. You’ll implement hyperparameter tuning, train multiple models, and complete real-world assignments.

涵盖的内容

10个视频1个作业

10个视频总计44分钟

Introduction to Kubeflow for ML Orchestration 2分钟
Different Components in Kubeflow Pipelines 4分钟
Lab : Deploy a simple pipeline for XgBoost Model 12分钟
Lab : Trigger Xgboost Model using compiled json for continuous training 4分钟
Lab : Execute end-to-end kubeflow pipeline with model evaluation 3分钟
Lab Assignment: Deploy a Scikit-Learn Credit Scoring Model to Vertex Pipelines 3分钟
Introduction to Vertex AI Experiments 2分钟
Lab:Use different model hyperparameters for Xgboost with Vertex AI Experiments 7分钟
Lab :Train Different Data science Classification models using Experiments 5分钟
Assignment : Perform Experiments for Bike share Regression Model 1分钟

1个作业总计15分钟

Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration - Assessment 15分钟

In this module, we will explore advanced techniques for hyperparameter tuning, model explainability, and versioning in Vertex AI. You’ll gain hands-on experience deploying and evaluating models with explainability features, while also managing different versions of your models.

涵盖的内容

17个视频1个作业

17个视频总计69分钟

Introduction to Hyperparameter Tuning on Vertex AI 2分钟
Lab : Implement Hyperparameter Tuning for BikeShare Regression Model 9分钟
Lab : Result Walkthrough & Assignment Overview 2分钟
Overview - VertexAI Explainability 3分钟
Lab : Deploy Model Endpoint With Explainability Parameters 4分钟
Lab: Execute explainability for Online predictions and Interpret results 5分钟
Lab: Execute explainability for Batch predictions and Interpret results 2分钟
Assignment : Perform Explainability for XgBoost Models 2分钟
Introduction to Model Versioning using Vertex AI Model Registry 2分钟
Lab : Deploy different versions of XgBoost Model to Model Registry 10分钟
Introduction to Vertex AI FeatureStore 3分钟
Lab : Create Feature store objects 4分钟
Lab : Ingest Data from Pandas DF into Feature Store 4分钟
Lab : Read Data From Vertex AI Feature Store into Pandas Df 6分钟
Introduction to AutoML 1分钟
Lab-Train and Deploy Classification Model using AutoML 5分钟
Lab - Train and Deploy Regression Model using AutoML 4分钟

1个作业总计15分钟

Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning - Assessment 15分钟

In this module, we will introduce you to the world of Generative AI on Google Cloud. You will learn about Google’s PaLM 2 language model and implement various generative AI applications, deploying models on Cloud Run and exploring hands-on labs.