Cloud-powered machine learning is now within reach for every data professional. This course teaches you to train, deploy, and monitor production-ready ML models using Google Vertex AI's AutoML platform — covering structured data, images, and text — entirely through the web console with no coding required.

AutoML: Build ML Models without Code
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What you'll learn
Set up Google Cloud Platform and Vertex AI to configure, upload datasets, and manage AutoML workflows for structured, image, and text data.
Train AutoML classification and regression models on structured data and interpret automated feature engineering and evaluation results
Build AutoML Vision and NLP models for image classification, object detection, and text sentiment analysis without writing any code
Deploy models for online predictions, connect outputs to Google Sheets and BigQuery, and monitor performance via the cloud console
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There are 4 modules in this course
Build a strong foundation in cloud-based no-code machine learning by setting up and navigating Google Cloud and Vertex AI for AutoML workflows. Explore cloud ML architecture, platform components, and the business value of scalable AI systems. This module prepares you to confidently train and interpret AutoML models while understanding the core concepts powering automated intelligence.
What's included
19 videos6 readings4 assignments
Advance your modeling capabilities by working with image, text, and reinforcement learning concepts using AutoML Vision and AutoML Natural Language. Learn to train image classification and object detection models, build sentiment analysis and text classification systems, and interpret performance metrics responsibly. By the end of this module, you will be able to select the right AutoML solution for diverse data types and align advanced AI techniques with practical business use cases.
What's included
8 videos4 readings4 assignments
Complete the end-to-end machine learning lifecycle by deploying, integrating, and managing models in production environments. Learn to choose between online and batch prediction strategies based on business requirements and performance constraints. Integrate AutoML outputs with tools like Google Sheets and BigQuery to operationalize insights in real workflows. This module equips you to move beyond experimentation and build scalable, production-ready AI systems that deliver measurable business value.
What's included
9 videos4 readings4 assignments
Consolidate your learning by revisiting the complete no-code AutoML lifecycle, from cloud platform setup and structured data modeling to advanced Vision, NLP, and reinforcement learning concepts. Reinforce key ideas in model training, evaluation, deployment strategies, business integration, and lifecycle management while demonstrating your ability to design, deploy, and monitor end-to-end machine learning solutions using Google Cloud Vertex AI through a comprehensive final assessment.
What's included
1 video1 reading2 assignments
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Frequently asked questions
AutoML automates the machine learning pipeline — including data preprocessing, feature engineering, model selection, and hyperparameter tuning — enabling anyone to build production-grade ML models without deep technical expertise or code.
Google Vertex AI is Google Cloud's unified ML platform that brings AutoML and custom ML tools together in one place. In this course, you'll use Vertex AI's AutoML capabilities entirely through the web-based console — no command-line or API usage required.
Ideal for data analysts, product managers, business intelligence professionals, domain experts, and non-technical teams who want to leverage cloud-based ML to automate predictions and integrate AI into real business workflows.
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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.


