A huge part of the machine learning process is experimentation, luckily there are a few Vertex AI features that can help you with tuning and scaling your ML models. In this video of Prototype to Production, Developer Advocate Nikita Namjoshi takes a look at hyperparameter tuning, distributed training, and experiment tracking. Watch this video to learn how you can get models out of experimentation and into production with Vertex AI.
Click on the video below to watch it in detail:
Chapters:
0:00 - Intro
0:49 - Hyperparameter tuning
1:28 - Hyperparameter tuning on Vertex AI
3:25 - Distributed training
5:16 - Configuring worker pools
6:00 - Experimentation with TensorBoard
7:03 - Vertex AI experiment tracking service
7:30 - Wrap up
Extra Credit:
- Hyperparameter tuning on Vertex AI docs → https://goo.gle/3RiRxpT
- Distributed training on Vertex AI docs → https://goo.gle/3QNgdXz
- Vertex AI TensorBoard docs → https://goo.gle/3CBiMb4
- Vertex AI Experiments docs → https://goo.gle/3pP0U4O
- Vertex AI Experiments notebooks → https://goo.gle/3Kw7cQj
