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

通过 Coursera Plus 解锁访问 10,000 多门课程。开始 7 天免费试用。

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

位教师：Packt - Course Instructors

包含在中

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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.

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

该课程共有8个模块

This course features Coursera Coach!

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11分钟

Hello & Introduction1分钟
Discord Server for this Course0分钟
Lab-Create GCP Trial Account for the course1分钟
Lab-Download gcloud-cli & project configuration2分钟
Course prerequisites and installations2分钟
Course Overview & section walkthrough2分钟
GCP Services used in the course1分钟

1篇阅读材料总计10分钟

Full Course Resources10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 GCP3分钟
Introduction to GCP Container Registry and Artifact Registry2分钟
Lab : Enable necessary APIs and install modules3分钟
Introduction To GCP CloudRun for ML Models1分钟
Overview of Steps for Flask Application - Local development1分钟
Lab : Deploy Flask application using Container/Artifact Registry and CloudRun12分钟
Lab: Execute PyTest locally using ChatGPT4分钟
Introduction to GCP CloudBuild Service1分钟
Lab : Deploy Flask application using GCP CloudBuild5分钟
Lab : Setup Cloudbuild Triggers from GitHub Repo5分钟
XGBoost Model Overview for Coupon Recommendations Model4分钟
Lab : Deploy and implement Model Serving Flask Application and Pytest Locally7分钟
Lab : Deploy ML Model to CloudRun using CloudBuild5分钟
Overview of A/B Testing for ML Models using CloudRun3分钟
Lab : Deploy New Version of ML Model & Update version traffic5分钟
Assignment - Deploy Bike Rentals Regression Model & perform CI/CD2分钟

1个作业总计15分钟

CI/CD using GCP CloudBuild, Artifact & Container Registry and CloudRun - Assessment15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 Campaign3分钟
Introduction to Continuous Training3分钟
Introduction to Airflow For Continuous Training1分钟
Lab: Create Setup Airflow composer Env and Vertex AI Workbench4分钟
Lab: Execute Model Training using Jupyter-Nbk on GCP5分钟
Lab: Execute Airflow Dag for Machine Learning Workflow8分钟
Lab : Continuous Training Pipeline in Action1分钟
Implications of Failure scenarios in Continuous Training2分钟
Lab: Trigger Continuous Training to capture model logs and setup alerting7分钟
Overview of CI/CD for Model Training1分钟
Lab : CI/CD of Model Training Code using Cloud-Build,PyTest and Github9分钟
Lab : Setup CloudBuild triggers3分钟
Assignment Part-1 : Setup Continuous Training for a Marketing ROI Model2分钟
Assignment Part-2 : Perform CICD of the Data Science ROI Model1分钟
Assignment Part-3 : Deploy Model Serving Application to GCP CloudRun2分钟

1个作业总计15分钟

Continuous Model Training using Cloud Composer-Airflow - Assessment15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 Overview1分钟
Introduction to Vertex AI Model Training Service3分钟
Overview of Bike Share Rentals Regression Model1分钟
Lab : Vertex AI Model Training using Web Console and Gcloud CLI11分钟
Introduction to Vertex AI Model Registry2分钟
Lab : Python SDK-Vertex AI Model Training,Model Registry and Model Deployment9分钟
Lab : Execute Online & Batch prediction Service using Python SDK and jupyter nbks4分钟
Lab-Walkthrough Batch Prediction Output & Online Prediction jobs using Cloud Run10分钟
Lab-Deploy and implement Batch Prediction Job using GCP Cloud Functions7分钟
Lab : Overview of CI/CD using Vertex AI2分钟
Lab : Vertex AI : CI/CD of Data science model using CloudBuild8分钟
Assignment : Deploy XGBoost Model to Vertex AI2分钟

1个作业总计15分钟

Vertex AI For Data Science & Machine Learning - Assessment15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43分钟

Introduction to Kubeflow for ML Orchestration2分钟
Different Components in Kubeflow Pipelines4分钟
Lab : Deploy a simple pipeline for XgBoost Model11分钟
Lab : Trigger Xgboost Model using compiled json for continuous training3分钟
Lab : Execute end-to-end kubeflow pipeline with model evaluation3分钟
Lab Assignment: Deploy a Scikit-Learn Credit Scoring Model to Vertex Pipelines2分钟
Introduction to Vertex AI Experiments2分钟
Lab:Use different model hyperparameters for Xgboost with Vertex AI Experiments6分钟
Lab :Train Different Data science Classification models using Experiments4分钟
Assignment : Perform Experiments for Bike share Regression Model1分钟

1个作业总计15分钟

Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration - Assessment15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 AI2分钟
Lab : Implement Hyperparameter Tuning for BikeShare Regression Model9分钟
Lab : Result Walkthrough & Assignment Overview2分钟
Overview - VertexAI Explainability3分钟
Lab : Deploy Model Endpoint With Explainability Parameters4分钟
Lab: Execute explainability for Online predictions and Interpret results4分钟
Lab: Execute explainability for Batch predictions and Interpret results2分钟
Assignment : Perform Explainability for XgBoost Models1分钟
Introduction to Model Versioning using Vertex AI Model Registry1分钟
Lab : Deploy different versions of XgBoost Model to Model Registry9分钟
Introduction to Vertex AI FeatureStore3分钟
Lab : Create Feature store objects3分钟
Lab : Ingest Data from Pandas DF into Feature Store3分钟
Lab : Read Data From Vertex AI Feature Store into Pandas Df6分钟
Introduction to AutoML1分钟
Lab-Train and Deploy Classification Model using AutoML4分钟
Lab - Train and Deploy Regression Model using AutoML4分钟

1个作业总计15分钟

Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning - Assessment15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.

涵盖的内容

8个视频3个作业

8个视频总计36分钟

Introduction to Generative AI4分钟
Introduction to Large language models - PaLM 21分钟
Important keywords and concepts in LLM2分钟
Lab-Generative AI Studio5分钟
Lab - Execute LLM using Python & Jupyter Nbk5分钟
Lab - Deploy text classification LLM Model using Python & Cloud Run6分钟
Lab-Deploy Document Summarization Application using Python & Cloud Run5分钟
Lab- Generate Fashion Image Descriptions using Python4分钟

3个作业总计90分钟

Generative AI on Google Cloud - Assessment15分钟
Full Course Assessment60分钟
Full Course Practice Assessment15分钟

位教师

Packt - Course Instructors

Packt

1,239 门课程296,477 名学生

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Packt

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常见问题

"Real-world End to End Machine Learning Ops on Google Cloud" is a comprehensive course designed to equip professionals with the skills needed to manage the complete lifecycle of machine learning (ML) models using Google Cloud services. It covers key concepts in ML Ops, including model deployment, continuous integration/continuous deployment (CI/CD), continuous model training, and model orchestration. This course is relevant because ML Ops is critical for maintaining scalable, reproducible, and automated ML pipelines, which are essential for businesses seeking to deploy and manage machine learning models in production environments.

This course guides learners through building and deploying end-to-end machine learning pipelines on Google Cloud. It introduces key concepts of ML Ops, from setting up CI/CD pipelines with tools like Cloud Build and Cloud Run, to implementing continuous model training with Cloud Composer and Airflow. You will also gain hands-on experience with Vertex AI for model training, deployment, and orchestration, as well as advanced techniques like hyperparameter tuning, model explainability, and versioning. The course is designed for anyone looking to implement machine learning models at scale in real-world environments.

Upon completion of this course, you'll be proficient in managing the full lifecycle of machine learning models on Google Cloud. You will be able to automate model deployment using CI/CD pipelines, set up continuous training workflows, deploy models using Vertex AI, and utilize Cloud Run for scalable model serving. You’ll also have experience with advanced model management techniques, including hyperparameter tuning, versioning, and explainability, equipping you to efficiently manage and monitor machine learning operations in production.

To get the most out of this course, you should have a basic understanding of machine learning concepts and workflows. Familiarity with Google Cloud services will also be helpful, though not mandatory, as the course will guide you through setting up necessary tools and accounts. Some knowledge of Python programming and machine learning libraries like XGBoost, as well as basic understanding of APIs and cloud computing, will be beneficial for the hands-on labs.

This course is ideal for data scientists, machine learning engineers, and software developers interested in gaining hands-on experience with ML Ops and deploying ML models at scale. It's especially useful for professionals aiming to integrate machine learning models into production systems using Google Cloud’s powerful tools and services. If you're looking to automate your ML workflows and manage models in a cloud environment, this course will provide you with the skills necessary to do so effectively.

The course contains approximately 6 hours of video content, which can be completed at your own pace. Depending on your prior knowledge and how much time you dedicate to the hands-on labs, it may take a few additional hours to complete assignments and exercises. The modular structure allows you to break it down into manageable sections, making it flexible for a variety of schedules.