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

Курс от Packt
Средний≈ 10.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Model DeploymentMLOps (Machine Learning Operations)Model TrainingCI/CDGoogle Cloud PlatformGenerative AIApache AirflowModel OptimizationModel EvaluationAI WorkflowsCloud ManagementContinuous IntegrationCloud ComputingCloud DeploymentLLM Application

Программа курса

8 модулей · 103 учебных материалов

01Introduction & prerequisites8 материалов

Introduction & prerequisites

Hello & IntroductionВидеоFull Course ResourcesЧтениеDiscord Server for this CourseВидеоLab-Create GCP Trial Account for the courseВидео

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Real-world End to End Machine Learning Ops on Google Cloud
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 10.7 ч

8 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Индонезийский, Испанский, Японский

Часть программы вашего университета
Lab-Download gcloud-cli & project configurationВидео
Course prerequisites and installationsВидео
Course Overview & section walkthroughВидео
GCP Services used in the courseВидео
02Introduction to ML Ops4 материалов

Introduction to ML Ops

Introduction To ML-OpsВидеоKey Components Principles in ML-OpsВидеоUnderstanding ML Ops and DevOps in Machine Learning DeploymentDIALOGUEIntroduction to ML Ops - AssessmentЗадание
03CI/CD using GCP CloudBuild, Artifact & Container Registry and CloudRun18 материалов

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

Introduction to CI/CD on GCPВидеоIntroduction to GCP Container Registry and Artifact RegistryВидеоLab : Enable necessary APIs and install modulesВидеоIntroduction To GCP CloudRun for ML ModelsВидеоOverview of Steps for Flask Application - Local developmentВидеоLab : Deploy Flask application using Container/Artifact Registry and CloudRunВидеоLab: Execute PyTest locally using ChatGPTВидеоIntroduction to GCP CloudBuild ServiceВидеоLab : Deploy Flask application using GCP CloudBuildВидеоLab : Setup Cloudbuild Triggers from GitHub RepoВидеоXGBoost Model Overview for Coupon Recommendations ModelВидеоLab : Deploy and implement Model Serving Flask Application and Pytest LocallyВидеоLab : Deploy ML Model to CloudRun using CloudBuildВидеоOverview of A/B Testing for ML Models using CloudRunВидеоLab : Deploy New Version of ML Model & Update version trafficВидеоAssignment - Deploy Bike Rentals Regression Model & perform CI/CDВидеоCI/CD for Data Science Model Deployment with Cloud Build & Cloud RunDIALOGUECI/CD using GCP CloudBuild, Artifact & Container Registry and CloudRun - AssessmentЗадание
04Continuous Model Training using Cloud Composer-Airflow17 материалов

Continuous Model Training using Cloud Composer-Airflow

Overview of Data science model for Bank Marketing CampaignВидеоIntroduction to Continuous TrainingВидеоIntroduction to Airflow For Continuous TrainingВидеоLab: Create Setup Airflow composer Env and Vertex AI WorkbenchВидеоLab: Execute Model Training using Jupyter-Nbk on GCPВидеоLab: Execute Airflow Dag for Machine Learning WorkflowВидеоLab : Continuous Training Pipeline in ActionВидеоImplications of Failure scenarios in Continuous TrainingВидеоLab: Trigger Continuous Training to capture model logs and setup alertingВидеоOverview of CI/CD for Model TrainingВидеоLab : CI/CD of Model Training Code using Cloud-Build,PyTest and GithubВидеоLab : Setup CloudBuild triggersВидеоAssignment Part-1 : Setup Continuous Training for a Marketing ROI ModelВидеоAssignment Part-2 : Perform CICD of the Data Science ROI ModelВидеоAssignment Part-3 : Deploy Model Serving Application to GCP CloudRunВидеоUnderstanding Continuous Training and DeploymentDIALOGUEContinuous Model Training using Cloud Composer-Airflow - AssessmentЗадание
05Vertex AI For Data Science & Machine Learning14 материалов

Vertex AI For Data Science & Machine Learning

Section OverviewВидеоIntroduction to Vertex AI Model Training ServiceВидеоOverview of Bike Share Rentals Regression ModelВидеоLab : Vertex AI Model Training using Web Console and Gcloud CLIВидеоIntroduction to Vertex AI Model RegistryВидеоLab : Python SDK-Vertex AI Model Training,Model Registry and Model DeploymentВидеоLab : Execute Online & Batch prediction Service using Python SDK and jupyter nbksВидеоLab-Walkthrough Batch Prediction Output & Online Prediction jobs using Cloud RunВидеоLab-Deploy and implement Batch Prediction Job using GCP Cloud FunctionsВидеоLab : Overview of CI/CD using Vertex AIВидеоLab : Vertex AI : CI/CD of Data science model using CloudBuildВидеоAssignment : Deploy XGBoost Model to Vertex AIВидеоUnderstanding Vertex AI and GCP Model DeploymentDIALOGUEVertex AI For Data Science & Machine Learning - AssessmentЗадание
06Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration12 материалов

Vertex AI-Kubeflow Pipelines for ML Workflow Orchestration

Introduction to Kubeflow for ML OrchestrationВидеоDifferent Components in Kubeflow PipelinesВидеоLab : Deploy a simple pipeline for XgBoost ModelВидеоLab : Trigger Xgboost Model using compiled json for continuous trainingВидеоLab : Execute end-to-end kubeflow pipeline with model evaluationВидеоLab Assignment: Deploy a Scikit-Learn Credit Scoring Model to Vertex PipelinesВидеоIntroduction to Vertex AI ExperimentsВидеоLab:Use different model hyperparameters for Xgboost with Vertex AI ExperimentsВидеоLab :Train Different Data science Classification models using ExperimentsВидеоAssignment : Perform Experiments for Bike share Regression ModelВидеоStructuring Qflow Pipelines and Using Experiments in Vertex AIDIALOGUEVertex AI-Kubeflow Pipelines for ML Workflow Orchestration - AssessmentЗадание
07Vertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning19 материалов

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

Introduction to Hyperparameter Tuning on Vertex AIВидеоLab : Implement Hyperparameter Tuning for BikeShare Regression ModelВидеоLab : Result Walkthrough & Assignment OverviewВидеоOverview - VertexAI ExplainabilityВидеоLab : Deploy Model Endpoint With Explainability ParametersВидеоLab: Execute explainability for Online predictions and Interpret resultsВидеоLab: Execute explainability for Batch predictions and Interpret resultsВидеоAssignment : Perform Explainability for XgBoost ModelsВидеоIntroduction to Model Versioning using Vertex AI Model RegistryВидеоLab : Deploy different versions of XgBoost Model to Model RegistryВидеоIntroduction to Vertex AI FeatureStoreВидеоLab : Create Feature store objectsВидеоLab : Ingest Data from Pandas DF into Feature StoreВидеоLab : Read Data From Vertex AI Feature Store into Pandas DfВидеоIntroduction to AutoMLВидеоLab-Train and Deploy Classification Model using AutoMLВидеоLab - Train and Deploy Regression Model using AutoMLВидеоUnderstanding Hyperparameter Tuning and Model ManagementDIALOGUEVertex AI-Hyperparameter Tuning Jobs, Explainability AI & Model Versioning - AssessmentЗадание
08Generative AI on Google Cloud11 материалов

Generative AI on Google Cloud

Introduction to Generative AIВидеоIntroduction to Large language models - PaLM 2ВидеоImportant keywords and concepts in LLMВидеоLab-Generative AI StudioВидеоLab - Execute LLM using Python & Jupyter NbkВидеоLab - Deploy text classification LLM Model using Python & Cloud RunВидеоLab-Deploy Document Summarization Application using Python & Cloud RunВидеоLab- Generate Fashion Image Descriptions using PythonВидеоGenerative AI on Google Cloud - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание