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

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

Dozent: Packt - Course Instructors

Bei enthalten

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8 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

9 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

8 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

9 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Apache Airflow
Kategorie: Continuous Deployment
Kategorie: Predictive Modeling
Kategorie: Kubernetes
Kategorie: Data Pipelines
Kategorie: Google Cloud Platform
Kategorie: Application Deployment
Kategorie: Generative AI
Kategorie: Machine Learning Methods
Kategorie: MLOps (Machine Learning Operations)
Kategorie: Performance Tuning
Kategorie: Continuous Integration
Kategorie: Containerization
Kategorie: Cloud Computing
Kategorie: Machine Learning
Kategorie: CI/CD

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Dezember 2025

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9 Aufgaben

Unterrichtet in Englisch

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In diesem Kurs gibt es 8 Module

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.

Das ist alles enthalten

7 Videos1 Lektüre

7 VideosInsgesamt 11 Minuten

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

1 LektüreInsgesamt 10 Minuten

Full Course Resources10 Minuten

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.

Das ist alles enthalten

2 Videos1 Aufgabe

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.

Das ist alles enthalten

16 Videos1 Aufgabe

16 VideosInsgesamt 70 Minuten

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

1 AufgabeInsgesamt 15 Minuten

CI/CD using GCP CloudBuild, Artifact & Container Registry and CloudRun - Assessment15 Minuten

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.

Das ist alles enthalten

15 Videos1 Aufgabe

15 VideosInsgesamt 59 Minuten

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

1 AufgabeInsgesamt 15 Minuten

Continuous Model Training using Cloud Composer-Airflow - Assessment15 Minuten

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.

Das ist alles enthalten

12 Videos1 Aufgabe

12 VideosInsgesamt 66 Minuten

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

1 AufgabeInsgesamt 15 Minuten

Vertex AI For Data Science & Machine Learning - Assessment15 Minuten

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.

Das ist alles enthalten

10 Videos1 Aufgabe

10 VideosInsgesamt 43 Minuten

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

1 AufgabeInsgesamt 15 Minuten

Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration - Assessment15 Minuten

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.

Das ist alles enthalten

17 Videos1 Aufgabe

17 VideosInsgesamt 69 Minuten

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

1 AufgabeInsgesamt 15 Minuten

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

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.

Das ist alles enthalten

8 Videos3 Aufgaben

8 VideosInsgesamt 36 Minuten

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

3 AufgabenInsgesamt 90 Minuten

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

Dozent

Packt - Course Instructors

Packt

1.218 Kurse295.149 Lernende

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Packt

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Häufig gestellte Fragen

"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.