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Preparing for Google Cloud Certification

Machine Learning Engineer

Google Cloud Training
87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine...
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87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification. Here's what you have to do 1) Complete the Preparing for Google Cloud Machine Learning Engineer Professional Certificate 2) Review other recommended resources for the Google Cloud Professional Machine Learning Engineer exam 3) Review the Professional Machine Learning Engineer exam guide 4) Complete Professional Machine Learning Engineer sample questions 5) Register for the Google Cloud certification exam (remotely or at a test center) Applied Learning Project This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.
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What's inside

Four courses

ML Pipelines on Google Cloud

(0 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

(0 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

(0 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

(0 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
1 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
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