You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket. What should you do?
You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket. What should you do?
The most efficient and straightforward approach for developing an image classification model with a large dataset stored in a Cloud Storage bucket is to import the labeled images as a managed dataset in Vertex AI and use AutoML to train the model. Vertex AI's AutoML simplifies the process by automating key steps such as preprocessing, model selection, and hyperparameter tuning, making it highly suitable for this task without the need for extensive custom pipeline development.
My answer: B TensorFlow Extended (TFX) and Kubeflow provide capabilities for building machine learning pipelines that can handle data stored in Google Cloud Storage (GCS). However, when it comes to ease of use specifically for working with data in GCS, TFX may have a slight edge over Kubeflow for 1- Integration with GCS- TensorFlow: TFX is tightly integrated with TensorFlow that has built-in support for GCS and provides convenient APIs for reading data directly from GCS buckets 2 - Abstraction of Data Handling TFX provides higher-level abstractions and components specifically designed for common machine learning tasks, including data preprocessing, model training, and model evaluation
Which SDK use? • If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data -> TFX • For other use-cases -> KFP
It's C
Very vaguely put. I choose C over B just because it sounds like a simpler approach, but both should theoretically work.
95th is the similar question. https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#sdk
95 is a similar question but it does not offer Vertex AI AutoML as an option. which I think it's the right answer here consider the little amount of info provided in the question
https://cloud.google.com/vertex-ai/docs/tutorials/image-classification-automl/dataset
no need to use a pipeline, automl is ok
B is right in my opinion, while both options C and B involve importing labeled images into Vertex AI, using AutoML for image classification might not be the most suitable choice. TFX is a more specialized tool that provides a robust pipeline framework specifically designed for image classification tasks, making it a better fit for this particular use case.