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Google Cloud Professional Machine Learning Engineer
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Google Cloud Professional Machine Learning Engineer
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Architecting low-code AI solutions 13%
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Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform
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Building AI solutions using Google Cloud AI APIs or foundational models
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Collaborating within and across teams to manage data and models 16%
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Exploring and preprocessing data for ML
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Model prototyping using notebooks
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Tracking and running ML experiments
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Scaling prototypes into ML models 21%
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Building models given the task considering cost, complexity, latency, and scalability
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Training models
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Choosing appropriate hardware for training
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Serving and scaling models 20%
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Serving models
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Scaling online model serving
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Automating and orchestrating ML pipelines 18%
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Developing end-to-end ML pipelines
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Automating model retraining
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Monitoring AI solutions 13%
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Identifying risks to AI solutions
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Monitoring, testing, and troubleshooting AI solutions
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