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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

Instructeur : Packt - Course Instructors

Inclus avec

8 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

9 heures à compléter

Planning flexible

Apprenez à votre propre rythme

8 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

9 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

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

Détails à connaître

Certificat partageable

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Récemment mis à jour !

décembre 2025

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9 devoirs

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Il y a 8 modules dans ce cours

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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.

Inclus

7 vidéos1 lecture

7 vidéosTotal 11 minutes

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

1 lectureTotal 10 minutes

Full Course Resources10 minutes

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.

Inclus

2 vidéos1 devoir

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.

Inclus

16 vidéos1 devoir

16 vidéosTotal 70 minutes

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

1 devoirTotal 15 minutes

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

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.

Inclus

15 vidéos1 devoir

15 vidéosTotal 59 minutes

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

1 devoirTotal 15 minutes

Continuous Model Training using Cloud Composer-Airflow - Assessment15 minutes

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.

Inclus

12 vidéos1 devoir

12 vidéosTotal 66 minutes

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

1 devoirTotal 15 minutes

Vertex AI For Data Science & Machine Learning - Assessment15 minutes

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.

Inclus

10 vidéos1 devoir

10 vidéosTotal 43 minutes

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

1 devoirTotal 15 minutes

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

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.

Inclus

17 vidéos1 devoir

17 vidéosTotal 69 minutes

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

1 devoirTotal 15 minutes

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

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.

Inclus

8 vidéos3 devoirs

8 vidéosTotal 36 minutes

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

3 devoirsTotal 90 minutes

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

Instructeur

Packt - Course Instructors

Packt

1 267 Cours297 713 apprenants

Offert par

Packt

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Foire Aux Questions

"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.