Vertex AI | Gemini | Generative AI – GCP Machine Learning — Course Overview & Key Highlights (2025)
Course introduction
Vertex AI | Gemini | Generative AI – GCP Machine Learning is a hands-on, practical course designed to help learners build, train, deploy, and scale machine learning and generative AI applications on Google Cloud Platform (GCP).
Created by Ankit Mistry, this course focuses on Vertex AI, AutoML, Gemini, prebuilt APIs (Vision, Language, Speech), custom model deployment, and ML pipelines—making it ideal for developers, data scientists, and cloud engineers who want real-world GCP ML skills.
Course details
- Instructor: Ankit Mistry
- Language: English
- Last updated: November 2025
- Duration: ~13.5 hours (13h 38m total)
- Rating: ★4.5 / 5
- Learners: 12,288+
- Price (typical sale): ~US$6–9 (varies by Udemy promotions)
- Access: Full lifetime access (mobile + TV)
- Certificate: Udemy Certificate of Completion included
Key highlights
- Learn Google Cloud Vertex AI from basics to deployment
- Work with Gemini & Generative AI concepts on GCP
- Use AutoML Vision & AutoML Language without heavy coding
- Hands-on labs with real ML training & prediction workflows
- Deploy Scikit-learn & TensorFlow models to production endpoints
- Build end-to-end ML pipelines using Vertex AI
- Learn batch prediction, custom containers, and scalable ML infra
What you will learn
Vertex AI & Google ML Products
- Understand Vertex AI ecosystem and use cases
- Choose the right ML product for each business scenario
- Use prebuilt ML APIs for vision, language, and speech
AutoML & Training
- Train models using AutoML Vision (image classification)
- Train models using AutoML Language (text classification)
- Submit datasets for training and evaluation
Model Deployment & Prediction
- Deploy Scikit-learn & TensorFlow models to endpoints
- Perform online and batch predictions
- Scale ML training and inference using GCP infrastructure
Advanced ML Workflows
- Use Vertex AI Workbench (Notebooks) for development
- Build ML pipelines for training, evaluation, and deployment
- Train models using custom containers & prebuilt containers
Frequently asked questions (FAQ)
Q — Is this course beginner-friendly?
A — Yes, but basic ML curiosity is expected. Concepts are explained step-by-step with hands-on demos.
Q — Do I need a Google Cloud account?
A — Yes. A GCP account with debit/credit card is required to access Vertex AI services.
Q — Does this course cover Generative AI?
A — Yes. It introduces Gemini and Generative AI concepts within the Vertex AI ecosystem.
Q — Will I learn real deployment, not just theory?
A — Absolutely. This course emphasizes real deployments, batch jobs, and endpoints.
Q — Is a certificate included?
A — Yes. You receive a Udemy Certificate of Completion.
Why this course is worth it
Most ML courses stop at model training. This course goes further—showing how to deploy, scale, and operationalize ML on Google Cloud. If your goal is to build production-ready ML systems using Vertex AI, this course delivers practical value.
Final verdict
If you want hands-on experience with Vertex AI, AutoML, and Generative AI on GCP, this course is a strong choice. It balances theory with practical labs and shows how real ML applications are built and deployed on Google Cloud.
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