Review the business rationale behind the need to build custom machine learning models.
- [Instructor] In this next section,…we're gonna take a look at building…custom machine learning models for deployment on GCP.…The important question to start with here,…is why we should build them at all.…In previous movies we looked at the APIs…and the new AutoML APIs and in many cases those are gonna…work for your business situation.…But in some cases they aren't.…So what are those cases?…You're gonna look first at the output type…or the accuracy of the model, so in a situation…where your output type isn't supported.…
For example, if you needed translation of a language…that wasn't part of the translation API…and an example of this I would give is an African…language I'm aware of which is called Bemba,…it's from Zambia that's not right now supported…for speech-to-text, you would have to create…a custom model for that.…If the data type wasn't supported…for example currently receipts are not supported…for the Vision AutoML API.…Another aspect would be if the prediction quality…of the model that was built automatically…
- 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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