Google Cloud Professional Data Engineer Cert Prep: 3 Operationalizing Machine Learning Models
58mAdvanced2023-07-21
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
Earning the Google Cloud Professional Data Engineer certification confirms that you’re able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. In this course, Noah Gift prepares you for the section of the exam that tests your knowledge on operationalizing machine learning models. Learn about leveraging pre-built ML models as a service, deploying an ML pipeline, choosing the appropriate training and serving infrastructure, and measuring, monitoring, and troubleshooting machine learning models.
Skills covered
Google CloudData EngineeringSoftware Development ToolsGoogleCloud PlatformsCert PrepCloud ComputingData ScienceSoftware Development
Concepts
0. Introduction
- 01 - Course overview
1. Pre-built ML Models as a Service
- 02 - Google Colab with TensorFlow Hub
- 03 - Using GCP NLP from the CLI
2. Training and Serving Infrastructure Selection
- 04 - PyTorch pretrained model overview
- 05 - Demo - PyTorch pretrained model
- 06 - Understanding TPUs
- 07 - TPUs as part of technology transition
- 08 - Getting started with Vertex AI
- 09 - Using GCP ML API vision from CLI
3. ML Model Measurement, Monitoring, and Troubleshooting
- 10 - Using the five whys method
- 11 - Demo - Load testing with Locust
- 12 - MLOps on GCP
Conclusion
- 13 - Using Google machine learning courses
- 14 - Next steps