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 are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do?
You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?
You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model’s underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?
You are developing an ML model in a Vertex Al Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?