Google Cloud Professional Data Engineer Cert Prep: 3 Operationalizing Machine Learning Models

Google Cloud Professional Data Engineer Cert Prep: 3 Operationalizing Machine Learning Models

58mAdvanced2023-07-21

Authors

Noah Gift

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 EngineeringGoogleSoftware Development ToolsCloud PlatformsCert PrepCloud ComputingData ScienceSoftware Development

Concepts

Introduction

  • Course overview

Pre-built ML Models as a Service

  • Google Colab with TensorFlow Hub
  • Using GCP NLP from the CLI

Training and Serving Infrastructure Selection

  • PyTorch pretrained model overview
  • Demo - PyTorch pretrained model
  • Understanding TPUs
  • TPUs as part of technology transition
  • Getting started with Vertex AI
  • Using GCP ML API vision from CLI

ML Model Measurement, Monitoring, and Troubleshooting

  • Using the five whys method
  • Demo - Load testing with Locust
  • MLOps on GCP

Conclusion

  • Using Google machine learning courses
  • Next steps
40,000 Toman