Review a help desk ticket application architecture that shows patterns and services for preparing data for machine learning and also see how to host a custom machine learning model.
- [Instructor] This next reference architecture…is interesting because of its use of machine learning,…but more importantly, because of its use of other services…in preparation and understanding of the data…prior to inputting it into…the machine learning model for training.…To me, this is very reflective of the real world.…As I've been saying multiple times in this course,…all those data sets we've been working with…are pristine and clean and really ready…to go for machine learning model training.…That is just not the case in the real world.…
So, I like this example because even though…it uses Google reference data,…it uses some other services…which I commonly use with customers…to visualize, clean and prepare the data,…so let's walk through this.…We start in the center at the cloud shell…and will work with a notebook instance…as we did in this course.…So we'll use Cloud Datalab…that is sitting on a GCE instance.…Now, when we're doing that,…we're capturing our work with the data…and we're experimenting as we're creating our 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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