Vertex Ai Pipelines: Production Mlops On Google Cloud
Published 3/2026
Created by Navid Shirzadi, Ph.D.
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 22 Lectures ( 3h 51m ) | Size: 2 GB
What you'll learn
✓ Build Production-Ready ML Pipelines
✓ Master MLOps Best Practices
✓ Create Reusable Pipeline Components
✓ Implement Advanced ML Workflows
✓ Handle Real-World ML Challenges
✓ Deploy Models to Google Cloud Platform
Requirements
● Machine Learning Concepts
● Basics of Python Programming
Description
Transform Your ML Models from Notebooks to Production with Google Cloud's Vertex AI
Are you tired of building machine learning models that never make it beyond Jupyter notebooks? Do you struggle with the gap between training a model and deploying it to production? This hands-on course bridges that critical divide by teaching you how to build automated, production-ready ML pipelines using Google Cloud's Vertex AI and Kubeflow Pipelines.
In this practical course, you'll move beyond basic model training to master the complete MLOps workflow. You'll learn to design modular, reusable pipeline components that automate every stage of the machine learning lifecycle-from data ingestion and validation through feature engineering, model training, evaluation, and deployment. No more manual steps, no more script chaos, no more deployment headaches.
Through a comprehensive real-world project and advanced hands-on exercise, you'll gain practical experience building increasingly sophisticated pipelines. You'll tackle real challenges like handling imbalanced datasets, implementing parallel model training, creating automated model comparison systems, and integrating with Vertex AI Model Registry for centralized model management. The project includes guided demonstrations and exercises with TODO items focused on MLOps patterns, while machine learning algorithms are pre-filled so you can concentrate on learning production workflows.
By course completion, you'll possess portfolio-ready work demonstrating your ability to automate ML workflows, implement quality gates, orchestrate complex pipelines, and deploy models on enterprise cloud infrastructure. You'll understand how companies like Google, Netflix, and Uber manage ML at scale, and you'll have transferable skills applicable to any cloud ML platform.
Whether you're a data scientist wanting to productionize your models, a software engineer entering the AI space, or an MLOps professional seeking hands-on GCP experience, this course provides the practical skills employers demand in AI-driven job market.
Stop building models that sit in notebooks. Start building ML systems that deliver real business value.
Who this course is for
■ Machine Learning Engineers & Data Scientists
■ MLOps Engineers & DevOps Professionals
■ Software Engineers
■ Cloud Architects & Solutions Architects
■ Graduate Students & Researchers
■ Career Changers with Python programming experience
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