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GCP Vertex AI Cloud Engineer (Generative AI & Machine Learning)
4.5/5

Level

Advanced

Duration

8 weeks

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What is GCP Vertex AI Cloud Engineer (Generative AI & Machine Learning)?

The GCP Vertex AI Cloud Engineer (Generative AI & Machine Learning) course is designed for professionals who want to architect, build, deploy, and manage end-to-end machine learning and generative AI solutions on Google Cloud. This program delivers in-depth coverage of Vertex AI fundamentals, data preparation, feature engineering, model training, hyperparameter tuning, and scalable deployment strategies. Learners gain hands-on expertise with AutoML, custom training using TensorFlow and PyTorch, model evaluation, monitoring, and full MLOps lifecycle automation using Vertex AI Pipelines. The course also emphasizes modern Generative AI capabilities, including large language models, embeddings, prompt engineering, and responsible AI practices. Real-world use cases, enterprise-grade architectures, and performance optimization techniques are deeply integrated to ensure job-ready skills. By the end of the course on Jast Tech, learners will confidently design secure, cost-efficient, and production-ready AI systems aligned with industry best practices and cloud engineering standards.

Job Roles You Can Achieve

After completing this course

  • Solutions Architect
  • Technical Consultant
  • Implementation Specialist
  • System Administrator
  • IT Professional

GCP Vertex AI Cloud Engineer (Generative AI & Machine Learning) Curriculum

1
Module 01

Google Cloud & Vertex AI Foundations

This module establishes cloud fundamentals and introduces Vertex AI as Google’s unified ML platform.

GCP core services, IAM, networking basics
Vertex AI architecture and components
2
Module 02

Data Preparation & Feature Engineering

Focuses on building reliable, scalable data pipelines for ML readiness.

BigQuery, Cloud Storage, Dataflow integration
Feature preprocessing and validation
3
Module 03

Vertex AI Datasets & Feature Store

Covers centralized feature management for consistency across models.

Managed datasets and schema design
Feature Store ingestion and reuse
4
Module 04

AutoML on Vertex AI

Demonstrates rapid model development with minimal code.

AutoML Tabular, Vision, NLP
Model evaluation and optimization
5
Module 05

Custom Model Training

Explains advanced model training using custom containers.

TensorFlow and PyTorch training jobs
Distributed and GPU/TPU training

Related Courses

Training Roadmap

Seven intentional milestones — from first session to dream job.

Onboarding

01
  • Meet your industry mentor
  • Define your goals
  • Skill gap assessment

Core Learning

02
  • Live interactive classes
  • AI-curated content
  • Recorded sessions

Hands-on Practice

03
  • Weekly assignments
  • MCQ evaluations
  • Module quizzes

Real Projects

04
  • 3 live industry projects
  • Portfolio building
  • Case studies

Mentorship

05
  • 1:1 doubt sessions
  • Peer collaboration
  • Expert feedback

Certification

06
  • Exam preparation
  • Practice dumps
  • Industry-recognised certificate

Career Launch

07
  • Resume crafting
  • Mock interviews
  • Job placement support

Key Projects

Hands-on experience with real-world scenarios designed for mastery.

Enterprise Customer Support Chatbot using Vertex AI

This project involves building a Generative AI-powered customer support chatbot using Vertex AI foundation models. The system uses embeddings for semantic search, prompt engineering for accurate responses, and secure APIs for real-time interaction. It demonstrates scalable deployment, monitoring, and responsible AI controls suitable for enterprise environments.

End-to-End ML Pipeline for Sales Forecasting

This project focuses on designing a complete MLOps pipeline using Vertex AI Pipelines to predict sales trends. It includes data ingestion from BigQuery, feature engineering, model training, hyperparameter tuning, automated deployment, and continuous monitoring, reflecting real-world business forecasting workflows.

Computer Vision Quality Inspection System

This project implements an image classification solution using Vertex AI AutoML Vision to detect product defects. It covers dataset labeling, model training, evaluation, batch predictions, and cost-optimized deployment, mirroring manufacturing quality assurance systems.

Available Course Schedules

Select a schedule that works best for you

Weekend

Starts

23 May 2026

Time

09:30 AM – 12:30 PM

Duration

8 weeks

Weekdays

Starts

25 May 2026

Time

07:00 AM – 09:00 AM

Duration

8 weeks

Weekend

Starts

30 May 2026

Time

02:00 PM – 05:00 PM

Duration

8 weeks

Weekdays

Starts

01 Jun 2026

Time

08:00 PM – 10:00 PM

Duration

8 weeks

Need a custom schedule?

Our team will craft the perfect batch for you.

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Expert resume crafting

Alumni Guidance

Mentorship from graduates

Interview Prep

Mock interviews & tips

Live Projects

Real-world experience

Review from Tejas Kumar

Tejas Kumar

Review from Sakshi Singh

Sakshi Singh

Review from Sanjay Patel

Sanjay Patel

Specialized Training Programs

JastTech For Corporates

JastTech Courses

Certification Details

GCP Vertex AI Cloud Engineer (Generative AI & Machine Learning) – Associate

  • Exam Name

    GCP Vertex AI Cloud Engineer (Generative AI & Machine Learning) – Associate

  • Exam Code

    SAA-C03

  • Duration

    130 minutes

  • Format

    Multiple Choice & Multi-Response

  • Passing Score

    720 (Scale: 100–1000)

  • Level

    Associate

Certificate of Completion

Prepare

Top Interview Questions

Curated questions with expert answers to help you ace your next interview.

Q1: What is Vertex AI and why is it important?

Vertex AI is Google Cloud’s unified ML platform that simplifies the entire ML lifecycle from data preparation to deployment and monitoring.

Q2: How does Vertex AI support MLOps?

It provides pipelines, metadata tracking, CI/CD integration, model versioning, and monitoring for production ML systems.

Q3: Difference between AutoML and custom training in Vertex AI?

AutoML requires minimal code and automates model selection, while custom training offers full control over frameworks and architectures.

Q4: How are Generative AI models deployed on Vertex AI?

Using managed endpoints, Model Garden APIs, or custom containers with scalable infrastructure.

Q5: What is responsible AI in Vertex AI?

It includes bias detection, explainability, monitoring, governance, and compliance to ensure ethical AI usage.

Support

Frequently Asked FAQs

Can't find what you're looking for? Reach out to our support team anytime.

Q1: Who should take this course?

Cloud engineers, ML engineers, data scientists, and architects aiming to work with Vertex AI and Generative AI.

Q2: Are coding skills mandatory?

Basic Python is recommended; advanced coding is taught progressively during the course.

Q3: Does this course cover Generative AI in depth?

Yes, including LLMs, embeddings, prompt engineering, and enterprise use cases.

Q4: Is this aligned with Google Cloud best practices?

Absolutely, it follows production-grade architectures and MLOps standards.

Q5: Will I work on real-world projects?

Yes, the course includes industry-aligned, hands-on projects.

The support team was very cooperative and responsive. They made sure all doubts were cleared without delay. Great experience overall.

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