GCP - Google Cloud Professional Machine Learning Engineer

Master Vertex AI, BigQuery ML, Generative AI, RAG, AI Agents & AutoML to Become a Google Cloud Professional ML Engineer

Become a job-ready Google Cloud Professional Machine Learning Engineer by mastering end-to-end Machine Learning, Generative AI, RAG architectures, and Agent development on Google Cloud Platform. This course is fully hands-on and covers the complete ML lifecycle — from SQL-based modeling with BigQuery ML to advanced AI systems using Vertex AI, Gemini models, and the Agent Development Kit (ADK).

You will start by building production-ready Machine Learning models using BigQuery ML, including regression, boosted trees, classification, recommendation systems, anomaly detection with autoencoders, time-series forecasting, and advanced feature engineering. From there, you will explore Vertex AI Model Garden to create text generation workflows, multimodal AI applications, image generation pipelines, and real-world AI solutions.

The course goes far beyond traditional ML by teaching you how to design intelligent systems powered by Generative AI. You will build complete projects using the Document AI API for large-scale document processing, develop AutoML pipelines for tabular, text, image, and forecasting workloads, and implement enterprise-grade AI agents using Google’s Agent Development Kit.

Inside the Agent Development section, you will create starter agents, tool-enabled agents, multi-agent systems, stateful workflows, persistent storage, callbacks, sequential and parallel agent architectures, and deploy production-ready agents to Vertex AI Agent Engine and Cloud Run.

You will also design modern enterprise AI architectures including:

• Retrieval-Augmented Generation (RAG) systems using Vertex AI RAG Engine

• Vector Search pipelines with embeddings, indexing, and querying

• Vertex AI Search implementations for enterprise search use cases

• Gemini File Search API projects to build RAG applications on your own data

• Feature Store pipelines for scalable online ML serving

In addition, you will learn how to build Generative AI applications using Google AI Studio, experiment with Gemini models, and create AI-powered applications without complex infrastructure.

Throughout the course, every concept is implemented through real-world, production-style demos, ensuring you gain practical skills aligned with the Google Cloud Professional Machine Learning Engineer certification and modern industry AI workflows.

Key topics covered:

• BigQuery ML (Regression, Classification, Boosted Trees, Forecasting, Recommendations, Autoencoders, Feature Engineering)

• Vertex AI Model Garden (Text Generation, Translation, Multimodal AI, Image Generation)

• Document AI API (Form Parsing, Batch Processing, JSON Extraction, Gradio Applications)

• Vertex AI AutoML (Tabular, Text, Image, Forecasting, Batch & Online Predictions)

• Agent Development Kit (Starter Agents, Tools, Stateful Multi-Agents, Callbacks, Sequential & Parallel Agents, Deployment)

• Vertex AI RAG Engine and RAG Agent Development

• Vertex AI Search and enterprise AI search systems

• Vertex AI Vector Search (Embeddings, Indexing, Querying, Maintenance)

• Vertex AI Feature Store (Online Serving, Feature Groups)

• Gemini File Search API for RAG applications

• Google AI Studio and Generative AI application development

This course is designed as a complete Google Cloud AI engineering roadmap, combining classical machine learning, Generative AI, agentic workflows, and enterprise search architectures into one structured learning path. Whether you are preparing for certification or building production-ready AI systems, you will gain the skills required to design, deploy, and scale machine learning solutions confidently on Google Cloud.