Exam Professional Machine Learning Engineer topic 1 question 194 discussion

'for ios mobile' = edge '.mlmodel directly' = minimizes the cost

No-code Training: AutoML Edge simplifies model training without needing extensive coding knowledge. On-device Processing: Core ML models run directly on the iOS device, minimizing latency by eliminating the need for network calls to a cloud endpoint. Cost-effective: Training on AutoML Edge and deploying the model on the device avoids ongoing costs associated with Vertex AI endpoints.

Core ML is specifically designed for iOS devices, ensuring efficient inference and low latency.

My Answer: B AutoML Edge or Vertex AI endpoint?: This option is specifically designed for training models that run on edge devices like mobile phones. It optimizes models for size and efficiency, minimizing cost and latency. While AutoML can train the model, using a Vertex AI endpoint adds unnecessary overhead and potential latency for mobile predictions. Batch requests wouldn't significantly improve latency here. Core ML or TFLite: While TFLite is compatible with some mobile platforms, Core ML is specifically designed for iOS and offers better performance and integration.

B. Confused as AutoML Vision Edge seems like the right tool for this problematic but is deprecated according to docs: https://firebase.google.com/docs/ml/automl-image-labeling I will assume that the question needs updating but we should go with that + core ML is specifically designed for iOS apps. https://www.netguru.com/blog/coreml-vs-tensorflow-lite-mobile

it's possible to use either Core ML or TF Lite, but since it's necessary to ensure the lowest possible latency, choose Core ML https://cloud.google.com/vertex-ai/docs/export/export-edge-model#classification

B For no code , automl is the best , for minimizing the cost we export as Core ML model

No-code model development: AutoML Edge provides a no-code interface for model training, aligning with the requirement. Optimized for mobile devices: Core ML is specifically designed for iOS devices, ensuring efficient inference and low latency. Offline capability: The app can run predictions locally without requiring network calls, reducing costs and ensuring availability even without internet connectivity. No ongoing endpoint costs: Unlike using a Vertex AI endpoint, there are no extra costs associated with hosting and serving the model.