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Google Professional-Machine-Learning-Engineer Exam
Google Professional Machine Learning Engineer
Last Updated: Jul 2, 2024
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271 Questions and Answers for the Google Professional-Machine-Learning-Engineer exam
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Google Professional Machine Learning Engineer Syllabus
- Architecting low-code ML solutions: It covers development of ML models by using BigQuery ML, using ML APIs to build AI solutions, and using AutoML to train models.
- 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.
- Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting suitable hardware for training.
- Serving and scaling models: Serving models and scaling online model serving are its sub-topics.
- 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.
- Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.