# Read the documentation (don’t focus on the code, but more on when to use which tool). Reading the whole GCP documentation can take forever so, focus on these specific areas:
- ML APIs — Vision, Natural Language , Video and Speech-to-text (understand what each one of them can do and also what they can’t)
- AutoML (learn when you should use AutoML instead of ML APIs)
- AI Platform (this is the most important part. Focus on how to improve performance, how to use accelerators such as TPUs and GPUs, how to do distributed training and serving and the different available tools, such as the What-If Tool)
- Recommendations AI (there are 3 model types. Learn when to use each of them)
- TPUs (know when you should use them, and how)
- TensorFlow (do not memorize code, but how to improve performance)
- BigQuery ML (learn what are all the available algorithms)
- https://cloud.google.com/certification/machine-learning-engineer
- https://docs.google.com/forms/d/e/1FAIpQLSeYmkCANE81qSBqLW0g2X7RoskBX9yGYQu-m1TtsjMvHabGqg/viewform
- https://cloud.google.com/certification/machine-learning-engineer
- https://cloud.google.com/docs
- https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform
- https://www.cloudskillsboost.google/paths/17
- https://github.com/sathishvj/awesome-gcp-certifications/blob/master/professional-machine-learning-engineer.md
- https://dzlab.github.io/certification/2022/01/08/gcp-ml-engineer-prep/
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