Become a Cloud Machine Learning developer in 5 steps
summary
Five concrete steps: export a Colab .ipynb, upload it to Vertex AI Workbench, switch to a TensorFlow 2 GPU kernel, swap a ResNet feature vector from TensorFlow Hub into a Keras head and save the trained model to a Cloud Storage bucket, then use notebook execution to run the job on Vertex AI managed training with a GPU and deploy the saved artifacts to an endpoint for low‑latency predictions — the demo successfully classifies images (e.g., rubber vine).
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chapters (9)
- 0:00Cloud ML development intro
- 1:30Five steps to production ML
- 2:56Demo setup and data prep
- 4:21Creating Vertex AI Workbench notebook
- 6:08Uploading and running notebook
- 7:14Serverless training with GPU
- 8:20Deploying model to endpoint
- 9:04Getting model predictions
- 10:08Summary and next steps
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