Learn about the implications of hosting choices for machine learning: serverless, containers, or virtual machines.
- [Instructor] So as we move into GCP Machine Learning,…we have a number of concepts to understand…as we get started.…The first thing is we need to understand…how the common machine learning algorithms map…to what GCP has made available for us,…and the way I like to group them is three types:…the first is what I call data-input type,…the second is what I call common,…and the third is what I call modern or complex.…So the data-input type, these are around business cases,…so you have data, and let's just take…the photo example again, where you wanna have labels…of animals in a photo,…and you may or may not have these photos that are labeled.…
What Google has provided is a number of APIs…or application programming interfaces,…or in-points basically where you can simply send data…from your GCP storage location,…usually a bucket, to this endpoint,…and, in the example of Vision,…you can get best case labels…from a GCP Machine Learning algorithm.…You don't have to select the algorithm,…you don't have to train a model,…
- Hosting options: Serverless, containers, and virtual machines
- Enabling the GCP ML AIs
- Preparing data with Cloud Dataflow and Dataprep
- Modeling predictions for images, video, text to speech, and cloud translation
- Machine learning with AutoML
- Advanced machine learning and deep learning
- Machine learning architectures
Skill Level Intermediate
1. Machine Learning on Google Cloud Platform
2. Machine Learning API Services
3. Machine Learning with AutoML
Understand AutoML Vision4m 57s
4. Advanced Machine Learning
5. Machine Learning Architectures
Next steps1m 30s
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