A managed ML training service can help you automate experimentation at scale or retain models for a production application. In this episode of Prototype to Production, Developer Advocate, Nikita Namjoshi, walks through the steps required to train custom models on Vertex AI. Watch along and learn about the benefits of a managed training service that helps keep your results fresh.
Chapters:
0:00 – Intro
0:22 – Why do I need a machine learning training service?
1:26 – What are containers?
2:19 – Update custom training code
3:23 – Cloud storage for machine learning
4:50 – Containerizing code for machine learning
5:39 – Dockerfile syntax
6:42 – How to store container images in Google Cloud
7:21 – How to launch a training job on Vertex AI
8:12 – Wrap up
Training a custom model on Vertex AI codelab → https://goo.gle/3w7kGvV
GCS Fuse on Vertex AI Training → https://goo.gle/3QtwxN0
Writing Dockerfiles for Vertex AI Training → https://goo.gle/3po8mDO
Vertex AI Training pre built containers → https://goo.gle/3JXYdXO
Vertex AI Training docs → https://goo.gle/3do3eN6
Prototype To Production playlist → https://goo.gle/PrototypeToProduction
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
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