AutoML: Build ML Models without Code
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AutoML: Build ML Models without Code
This course is part of No-Code Data Science and Machine Learning Specialization
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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
Skills you'll gain
- Natural Language Processing
- Deep Learning
- Reinforcement Learning
- Machine Learning Software
- Cloud Deployment
- Image Analysis
- Model Training
- Machine Learning
- Applied Machine Learning
- Predictive Modeling
- Google Cloud Platform
- Cloud Platforms
- Data Science
- Model Evaluation
- Convolutional Neural Networks
- Feature Engineering
- Computer Vision
Tools you'll learn
Details to know
March 2026
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There are 4 modules in this course
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.
Throughout this course, you'll move through the complete AutoML lifecycle: platform setup, dataset management, advanced model training across vision and NLP domains, real-world deployment, and business tool integration — all backed by step-by-step video demonstrations on Google Cloud. You're expected to set up your own Google Cloud account, follow along with each instructor demonstration in the console, and pause the video as needed to complete each configuration or training step at your own pace. By the end of this course, you'll be able to: - Configure Google Cloud Platform and Vertex AI to set up and manage AutoML workflows for structured, image, and text datasets. - Train classification and regression models using AutoML Tables and interpret automated feature engineering and model evaluation results. - Build and evaluate AutoML Vision and Natural Language models for image classification, object detection, and text sentiment analysis. - Deploy trained models for online predictions, integrate outputs with Google Sheets and BigQuery, and monitor model performance through the cloud console. This course is designed for a diverse audience: data analysts, business intelligence professionals, product managers, domain experts, and non-technical professionals looking to leverage cloud ML capabilities to automate predictions and integrate AI into business workflows. Basic familiarity with data and machine learning concept, is recommended before enrolling. Step into cloud-powered ML and master the skills to build, deploy, and manage intelligent AutoML models that deliver measurable business impact — without writing a single line of code.
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
19 videos•Total 84 minutes
- Course Introduction•3 minutes
- Cloud Machine Learning Platform Overview•5 minutes
- Benefits and Business Value of Cloud Machine Learning•6 minutes
- Platform Architecture and Workflow•5 minutes
- AutoML Capabilities and Features•5 minutes
- Hands-On: Configuring Google Cloud Platform for Machine Learning•5 minutes
- Hands-On: Setting Up Vertex AI Environment for AutoML Workflows•4 minutes
- Hands-On: Uploading and Managing Datasets in AutoML Console•3 minutes
- Data Preparation & Automated Feature Engineering•4 minutes
- Automated Model Selection, Training & Evaluation•4 minutes
- Hands-On: Training Your First Classification Model with AutoML Tables (Web UI)•4 minutes
- Hands-On: Training Your First Regression Model with AutoML Tables (Web UI)•6 minutes
- Ensemble Learning & Gradient Boosting•5 minutes
- XGBoost - Optimized Gradient Boosting in Practice•5 minutes
- Deep Learning & Neural Network Fundamentals•4 minutes
- Convolutional Neural Networks (CNNs)•4 minutes
- Recurrent Neural Networks (RNNs)•3 minutes
- Hands-On: Interpreting Deep Neural Network Outputs in AutoML Vision•6 minutes
- Hands-On: Analyzing CNN-Based Image Classification Results in AutoML Vision•4 minutes
6 readings•Total 60 minutes
- Course Outline: Complete No-Code Advanced ML & Deployment Journey •10 minutes
- Reading: Cloud ML and AutoML Platform Setup•10 minutes
- Reading: AutoML Tables for Structured Data•10 minutes
- Exploring and Interpreting Ensemble Models in AutoML Reports•10 minutes
- Reading: Understanding Advanced ML Concepts with Demonstrations•10 minutes
- Module Summary: AutoML Platform Setup and Hands-On Experience•10 minutes
4 assignments•Total 33 minutes
- Graded Assignment: AutoML Platform Setup and Hands-On Experience•15 minutes
- Practice Assignment : Cloud ML and AutoML Platform Setup•6 minutes
- Practice Assignment : AutoML Tables for Structured Data•6 minutes
- Practice Assignment: Understanding Advanced ML Concepts with Demonstrations•6 minutes
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
8 videos•Total 35 minutes
- Reinforcement Learning Fundamentals: Agents, Actions, Rewards and Q-Learning•5 minutes
- Real-World RL Applications•4 minutes
- Computer Vision Fundamentals•5 minutes
- Hands-On: Uploading Image Datasets and Labeling in AutoML Vision•3 minutes
- Hands-On: Training Image Classification Models (Web UI)•5 minutes
- Hands-On: Training Object Detection Models and Evaluating Results•3 minutes
- NLP Fundamentals: Text Classification and Entity Extraction•4 minutes
- Hands-On: Training Text Classification and Sentiment Analysis Models (Web UI)•5 minutes
4 readings•Total 40 minutes
- Reading: Reinforcement Learning Concepts with Demonstrations•10 minutes
- Reading: AutoML Vision for Image Data•10 minutes
- Reading: AutoML Natural Language for Text Data•10 minutes
- Module Summary: Advanced Model Training - Vision, NLP and RL Concepts•10 minutes
4 assignments•Total 33 minutes
- Graded Assignment: Advanced Model Training - Vision, NLP & RL Concepts•15 minutes
- Practice Assignment: Reinforcement Learning Concepts with Demonstrations•6 minutes
- Practice Assignment : AutoML Vision for Image Data•6 minutes
- Practice Assignement: AutoML Natural Language for Text Data•6 minutes
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
9 videos•Total 37 minutes
- Model Deployment Options•4 minutes
- Hands-On: Deploying Models for Online Predictions (Web UI)•4 minutes
- Hands-On: Making Predictions Using AutoML Console and Testing Models•4 minutes
- Integrating AutoML with Business Tools•4 minutes
- Hands-On: Connecting AutoML Predictions to Google Sheets (No-Code)•4 minutes
- Hands-On: Using BigQuery ML with AutoML for Data Analysis (UI Only)•5 minutes
- Model Monitoring: Performance Tracking and Model Lifecycle•4 minutes
- Hands-On: Monitoring Model Performance in Cloud Console•4 minutes
- Hands-On: Retraining Models with New Data in AutoML (Web UI)•5 minutes
4 readings•Total 40 minutes
- Reading: Deploying Models with AutoML•10 minutes
- Reading: No-Code Business Tool Integration•10 minutes
- Reading: Monitoring and Managing Models•10 minutes
- Module Summary: Model Deployment and Business Integration•10 minutes
4 assignments•Total 33 minutes
- Graded Assignment: Model Deployment and Business Integration•15 minutes
- Practice Assignment : Deploying Models with AutoML•6 minutes
- Practice Assignment : No-Code Business Tool Integration•6 minutes
- Practice Assignment: Monitoring and Managing Models•6 minutes
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
1 video•Total 3 minutes
- Course Summary•3 minutes
1 reading•Total 30 minutes
- Practice Project: Building an End-to-End AutoML Intelligence System for NovaRetail Group•30 minutes
2 assignments•Total 60 minutes
- Knowledge Check: Complete No-Code Advanced ML & Deployment Journey•30 minutes
- Enterprise-Scale AutoML Implementation and Deployment Strategy•30 minutes
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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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