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

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22-07-2023, 11:50
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  • Google Cloud Professional Data Engineer Cert Prep – 3 Operationalizing Machine Learning Models
    Free Download Google Cloud Professional Data Engineer Cert Prep – 3 Operationalizing Machine Learning Models
    Released: 07/2023
    Duration: 58m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 148 MB
    Level: Advanced | Genre: eLearning | Language: English
    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.

Google Cloud Professional Data Engineer Cert Prep – 3 Operationalizing Machine Learning Models
Free Download Google Cloud Professional Data Engineer Cert Prep – 3 Operationalizing Machine Learning Models
Released: 07/2023
Duration: 58m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 148 MB
Level: Advanced | Genre: eLearning | Language: English
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.


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