ML workflow using Vertex AI | AutoML | Google Cloud | ML Project | Tutorial | step by step

In this video, I walk through a complete machine learning workflow using Google Vertex AI AutoML to build a predictive model for a real-world problem. We use the Combined Cycle Power Plant dataset to predict electrical energy output based on environmental factors like temperature, pressure, humidity, and exhaust vacuum. This is a beginner-friendly, step-by-step tutorial covering everything from dataset upload to model training and evaluation. 🚀 What you’ll learn: How to use Google Vertex AI Difference between regression and classification Uploading and preparing tabular datasets Training a model using AutoML Evaluating performance (RMSE, R², MAE) Understanding feature importance Real-world application of ML in energy systems 📊 Project Overview: Dataset: Combined Cycle Power Plant Features: Temperature, Pressure, Humidity, Vacuum Target: Electrical Power Output (PE) Model Type: Tabular Regression Platform: Google Vertex AI AutoML 🔗 Access Google Vertex AI: https://console.cloud.google.com/vertex-ai 💡 Why this project matters: This project shows how machine learning can be used to predict energy generation, improve efficiency, and support smarter decision-making in power systems. 🛠 Tools Used: Google Cloud Platform (GCP) Vertex AI AutoML (Tabular) ✨ About Me: Hi, I’m Ananya — a UX designer exploring AI, machine learning, and human-centered tech. #MachineLearning #VertexAI #GoogleCloud #AutoML #MLProject #DataScience #AI #Regression #BeginnerFriendly