Summary
Here is a summary of the essential steps to consider when working with big data and machine learning on Google Cloud:
- Acknowledge the life cycle of data-to-AI transformation on Google Cloud
- Identify the connection between data engineering and machine learning
- Identify the options available to build ML models on Google Cloud.
- Determine the major features and benefits of Vertex AI.
Key Knowledge and Tips
- TPUs (Tensor Processing Units) is Google hardware innovation that tailors architecture to meet the computation needs on a domain, such as the matrix multiplication in machine learning
- Archive storage is the data storage class that is best for storing data that needs to be accessed less than once a year, such as online backups and disaster recovery
- Compute Engine, Google Kubernetes Engine, App Engine, and Cloud Functions represent Compute services
- Cloud Storage, Cloud Bigtable, Cloud SQL, Cloud Spanner, and Firestore represent Database and storage services
- Pub/Sub, Dataflow, Dataproc, and Cloud Data Fusion align to the Ingestion and process stage of the data-to-AI workflow
- AutoML, Vertex AI Workbench, and TensorFlow align to the Machine learning stage of the data-to-AI workflow
We offer practical recommendations in this report. If you have any projects related to Big Data and Machine Learning in Google Cloud, please feel free to contact us.
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