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Overview
The Google Machine Learning Engineer certification prepares individuals to build, deploy, and optimize AI and ML solutions on Google Cloud. This course emphasizes practical skills in data engineering, model deployment, and MLOps, enabling engineers to create scalable AI solutions and integrate generative AI tools, such as Model Garden and Vertex AI.
Objectives
By the end of this course, leaner will be able to:
- Architect low-code and custom AI solutions on Google Cloud.
- Scale and serve production-ready ML models.
- Automate and orchestrate ML pipelines for efficient deployment.
- Monitor and retrain models to maintain AI performance.
- Integrate generative AI tools and uphold responsible AI practices.
Prerequisites
- 3+ years of industry experience in AI/ML.
- 1+ year of experience with Google Cloud ML tools and infrastructure.
- Proficiency in Python and foundational SQL for ML model development.
- Familiarity with MLOps practices and AI monitoring.
- Basic understanding of data processing and distributed data platforms.
Course Outline
Module 1 : Data Engineering and Model DevelopmentCourses2024-11-02T19:08:24+05:30
- Data preparation, feature engineering, and foundational model selection.
Module 2 : Generative AI and Google CloudCourses2024-11-02T19:09:25+05:30
- Introduction to Model Garden and Vertex AI Agent Builder for generative AI integration.
Module 3 : ML Pipeline AutomationCourses2024-11-02T19:10:24+05:30
- Orchestrate pipelines with Vertex AI; automate model retraining and deployment workflows.
Module 4 : Scaling and Monitoring ML ModelsCourses2024-11-02T19:11:14+05:30
- Techniques for model scaling, AI monitoring, and implementing responsible AI practices.
Module 5 : MLOps and Continuous ImprovementCourses2024-11-02T19:12:08+05:30
- Explore MLOps strategies for sustainable ML model lifecycle and infrastructure management.
