Review the landscape of GCP machine learning services, including SaaS or serverless services (ML APIs and AutoML), PaaS or containers (Cloud ML) and IaaS or virtual machines.
- [Lynn] So in this course we've covered…quite a bit of ground looking…at Google Cloud Platform machine learning services.…We look at services available in the SaaS layer,…such as Vision, Video, and Auto ML for Vision,…Natural Language, Translation, and others.…We looked at services in the PaaS layer,…native containers and managed containers,…particularly ML Engine.…We also looked at using virtual machines…and some of the newer hardware available,…such as GPUs and TPUs.…
Finally, we looked at architectures…that mix some or all of these services together…to create powerful applications…that use machine learning effectively.…If you wanna learn more,…in addition to working with the Google resources,…I recommend that you check out the community…of external experts.…These are called Google Cloud Platform Developer Experts…or GDEs, Google Developer Experts.…In addition to cloud experts,…there's actually a new category very, very related…to this course, and that is GDEs for machine learning.…
There are only four in the world as of this recording.…
- 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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