Google Professional-Machine-Learning-Engineer 考試大綱：

主題	簡介
主題 1	Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting suitable hardware for training.
主題 2	Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
主題 3	Automating and orchestrating ML pipelines: This topic focuses on development of end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.
主題 4	Collaborating within and across teams to manage data and models: It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter notebooks to model prototype. Lastly, it discusses tracking and running ML experiments.

參考：https://cloud.google.com/certification/guides/machine-learning-engineer

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