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

سرفصل های دوره

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.

01 - Introduction

01 - Course overview

02 - 1. Pre-built ML Models as a Service

01 - Google Colab with TensorFlow Hub

02 - Using GCP NLP from the CLI

03 - 2. Training and Serving Infrastructure Selection

01 - PyTorch pretrained model overview

02 - Demo PyTorch pretrained model

03 - Understanding TPUs

04 - TPUs as part of technology transition

05 - Getting started with Vertex AI

06 - Using GCP ML API vision from CLI

04 - 3. ML Model Measurement, Monitoring, and Troubleshooting

01 - Using the five whys method

02 - Demo Load testing with Locust

03 - MLOps on GCP

05 - Conclusion

01 - Using Google machine learning courses

02 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)