In what scenarios would one choose batch predictions over real-time (online) predictions when serving a machine learning model on Google Cloud, and what are the trade-offs of each approach?
When deciding between batch predictions and real-time (online) predictions on Google Cloud for serving a machine learning model, it's important to consider the specific requirements of your application, as well as the trade-offs associated with each approach. Both methodologies have distinct advantages and limitations that can significantly impact performance, cost, and user experience. Batch Predictions
How did the speaker chunk the list of image slices into a fixed number of chunks?
The speaker chunked the list of image slices into a fixed number of chunks using a technique called batch processing. In the context of deep learning with TensorFlow and the Kaggle lung cancer detection competition, this process involves dividing the dataset into smaller groups or batches for efficient processing by a 3D convolutional neural network
What is the purpose of padding in text classification and how does it help in training a neural network?
Padding is a important technique used in text classification tasks to ensure that all input sequences have the same length. It involves adding special tokens, typically zeros or a specific padding token, to the beginning or end of the sequences. The purpose of padding is to create uniformity in the input data, enabling efficient batch
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Text classification with TensorFlow, Preparing data for machine learning, Examination review
What are the different methods available to create Dataflow jobs?
There are several methods available to create Dataflow jobs in Google Cloud Platform (GCP). Dataflow is a fully managed service for executing batch and streaming data processing pipelines. It provides a flexible and scalable way to process large amounts of data in parallel, making it ideal for big data analytics and real-time data processing. 1.
- Published in Cloud Computing, EITC/CL/GCP Google Cloud Platform, GCP basic concepts, Dataflow, Examination review
What are some use cases for Compute Engine?
Compute Engine is a fundamental component of Google Cloud Platform (GCP) that enables users to run virtual machines (VMs) in the cloud. It provides a reliable and scalable infrastructure for various use cases, offering flexibility and control over computing resources. In this answer, we will explore some of the prominent use cases for Compute Engine,
- Published in Cloud Computing, EITC/CL/GCP Google Cloud Platform, GCP basic concepts, Compute Engine, Examination review