Video Training / IT and Programming for PC Tutorials →GCP - Google Cloud Professional Machine Learning Engineer

Master BigQuery ML, Vertex AI, AutoML, Document AI, ADK, and RAG to Become a Google Cloud Professional ML Engineer
What you'll learn
Build end-to-end machine learning solutions on Google Cloud Platform (GCP)
Develop ML models using BigQuery ML (Regression, Classification, Boosted Trees, Time-Series, Autoencoders, Recommendations)
Use Vertex AI Model Garden to build generative AI, multimodal, and text-processing applications
Implement intelligent document processing workflows using Document AI API and build a Gradio-based Form Parsing App
Train, evaluate, deploy, and run predictions using Vertex AI AutoML (Tabular, Text, Image, Forecasting
Build production-grade LLM Agents using the Agent Development Kit (ADK), including tools, multi-agents, callbacks, and persistent storage
Develop Retrieval-Augmented Generation (RAG) pipelines using Vertex AI RAG Service, including corpus creation, querying, and agent integration
Apply best practices for ML model development, deployment, monitoring, and optimization on GCP
Requirements
Basic understanding of Python (beginner level is enough)
Familiarity with machine learning fundamentals (optional but helpful)
A Google Cloud account (free tier is sufficient for most hands-on labs)
Curiosity to learn AI, ML, Generative AI, and cloud-based development
Description
Become a job-ready Google Cloud Professional Machine Learning Engineer by learning end-to-end ML, GenAI, and agent development on GCP. This course is fully hands-on and covers the entire ML lifecycle using BigQuery ML, Vertex AI Model Garden, Document AI, AutoML, the Agent Development Kit (ADK), and Vertex AI RAG Service.You'll build real-world projects including regression models, classification systems, recommendation engines, anomaly detectors, time-series forecasting models, generative AI applications, document parsers, AutoML pipelines, and complete LLM agents with tools, state management, persistent storage, and callbacks.You will also create enterprise-grade Retrieval-Augmented Generation (RAG) systems, integrate LLMs with custom corpora, and build a fully functional RAG agent using ADK + Vertex AI RAG Service.By the end of this course, you will be able to confidently design, build, deploy, and monitor machine learning solutions on GCP—skills directly aligned with the Google Cloud Professional ML Engineer certification.Key topics covered:BigQuery ML (Regression, Classification, Boosted Trees, Time-Series, Autoencoders, Recommendations, Feature Engineering)Vertex AI Model Garden (Generative AI, Multimodal AI, Image Generation, Text Processing)Document AI API (Form Parsing, PDF Processing, JSON Extraction, Gradio App)Vertex AI AutoML (Tabular, Image, Text, Forecasting)Agent Development Kit (Starter agents, Tools, Multi-Agents, State, Runners, Callbacks, Sequential & Parallel Agents)Vertex AI RAG Service (Corpus creation, querying, RAG agent development)This course gives you practical, project-based learning—every concept is implemented live, making it ideal for both beginners and experienced professionals aiming to master ML on Google Cloud.
Who this course is for
Aspiring Google Cloud Professional Machine Learning Engineer certification candidates
Data Scientists, ML Engineers, AI Engineers, and Developers transitioning to Google Cloud
Professionals who want hands-on experience with BigQuery ML, Vertex AI, AutoML, Document AI, and RAG
Anyone who wants to build real, production-ready AI/ML and GenAI applications on GCP
Students and tech enthusiasts looking to enter the field of cloud-based machine learning
Homepage
https://www.udemy.com/course/gcp-google-cloud-professional-machine-learning-engineer/