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Preparing for Google Cloud Certification

Machine Learning Engineer

Google Cloud Training

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What's inside

Four courses

ML Pipelines on Google Cloud

(4 hours)

In this course, you will learn about TensorFlow Extended (TFX), Google's production machine learning platform for managing ML pipelines and metadata. You will also learn how to automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.

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Production Machine Learning Systems

(18 hours)

In this course, we explore the components and best practices for building high-performing ML systems in production environments. We cover considerations such as static and dynamic training and inference, distributed TensorFlow, and TPUs. This course focuses on the characteristics of effective ML systems beyond their predictive capabilities.

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Machine Learning Operations (MLOps): Getting Started

(2 hours)

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.

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Google Cloud Big Data and Machine Learning Fundamentals

(9 hours)

This course introduces Google Cloud's big data and machine learning offerings, covering the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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Time

1980 hours

Via

Coursera

Institution

Google Cloud

Instructor

Google Cloud Training

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