Google Cloud Professional Machine Learning Engineer Cert Prep: 2 Architecting ML Solution
1h 27mAdvanced2023-06-09
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
Earning a Google Professional Machine Learning Engineer certification demonstrates your ability to design, build, and productionize machine learning models to solve business challenges using Google Cloud technologies, and knowledge of proven ML models and techniques.
In this second course in the certification prep series, instructor Noah Gift covers topics relating to architecting machine learning solutions. He shows you how to design reliable, scalable, and highly-available ML solutions, covering topics like continuous delivery, reproducible workflow, and continuous integration. Noah then explains how to choose the appropriate Google Cloud hardware components for your ML solutions.
In this second course in the certification prep series, instructor Noah Gift covers topics relating to architecting machine learning solutions. He shows you how to design reliable, scalable, and highly-available ML solutions, covering topics like continuous delivery, reproducible workflow, and continuous integration. Noah then explains how to choose the appropriate Google Cloud hardware components for your ML solutions.
Skills covered
Google CloudSoftware ArchitectureMachine LearningSoftware Development ToolsGoogleCloud PlatformsCert PrepArtificial Intelligence (AI)Cloud ComputingSoftware Development
Concepts
0. Introduction
- 01 - Overview
- 02 - Course 2 key terminology
- 03 - Cloud developer workspace advantage
1. Designing a Reliable, Scalable, and Highly Available ML Solution
- 04 - What is continuous delivery
- 05 - Containerized ML microservices
- 06 - SRE mindset for MLOps
- 07 - Reproducible workflow
- 08 - Learn continuous integration
2. Choosing Appropriate Google Cloud Hardware Components
- 09 - Selecting heavy vs. light MLOps
- 10 - Key components of MLOps landscape
- 11 - Feature store vs. data warehouse
- 12 - Compute choice
Conclusion
- 13 - Next steps
Related courses
- Google Cloud Professional Machine Learning Engineer Cert Prep
- Google Cloud Professional Machine Learning Engineer Cert Prep: 1 Framing ML Problems
- Google Cloud Professional Machine Learning Engineer Cert Prep: 3 Designing Data Preparation and Processing Systems
- Google Cloud Professional Machine Learning Engineer Cert Prep: 4 Developing ML Models
- Google Cloud Professional Machine Learning Engineer Cert Prep: 5 Automating and Orchestrating ML Pipelines
- Google Cloud Professional Machine Learning Engineer Cert Prep: 6 Monitoring, Optimizing, and Maintaining ML Solutions
- Google Cloud Professional Data Engineer Cert Prep: 3 Operationalizing Machine Learning Models
- Google Cloud Professional Data Engineer Cert Prep (2025)
Related learn paths
- Prepare for the Google Cloud Professional Machine Learning Engineer Certification
- Prepare for the Google Cloud Professional Data Engineer Certification
- Fundamentals to Become a Machine Learning Engineer
- Become an AI Engineer
- Develop Your AI Skills with Google Gemini and Google Cloud Platform
- Working with Data: Engineering, Integration, and MLOps for AI
- Building AI Products: Architecture and Orchestration Professional Certificate by LinkedIn Learning
- MLOps Essentials for Developers and AI Engineers: Tools, Pipelines, Security