How to use Fashion-MNIST dataset in Google Cloud Machine Learning / AI Platform?
Fashion-MNIST is a dataset of Zalando's article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes. The dataset serves as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms,
What is classifier?
A classifier in the context of machine learning is a model that is trained to predict the category or class of a given input data point. It is a fundamental concept in supervised learning, where the algorithm learns from labeled training data to make predictions on unseen data. Classifiers are extensively used in various applications
Can TensorBoard be used online?
Yes, one can use TensorBoard online for visualizing machine learning models. TensorBoard is a powerful visualization tool that comes with TensorFlow, a popular open-source machine learning framework developed by Google. It allows you to track and visualize various aspects of your machine learning models, such as model graphs, training metrics, and embeddings. By visualizing these
Can one utilize the configuration file for the CMLE model deployment when using a distributed ML model training to define how many machines will be used in training?
When using distributed machine learning (ML) model training on Google Cloud AI Platform, you can indeed utilize the configuration file for the CMLE (Cloud Machine Learning Engine) model deployment to define the number of machines used in training. However, it is not possible to directly define the type of machines that will be used. In
What are the deployment targets for the Pusher component in TFX?
The Pusher component in TensorFlow Extended (TFX) is a fundamental part of the TFX pipeline that handles the deployment of trained models to various target environments. The deployment targets for the Pusher component in TFX are diverse and flexible, allowing users to deploy their models to different platforms depending on their specific requirements. In this
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow Extended (TFX), Distributed processing and components, Examination review
How can the BLEU score be used to evaluate the performance of a custom translation model trained with AutoML Translation?
The BLEU score is a widely used metric for evaluating the performance of machine translation models. It measures the similarity between a machine-generated translation and one or more reference translations. In the context of a custom translation model trained with AutoML Translation, the BLEU score can provide valuable insights into the quality and effectiveness of
What are the steps involved in creating a custom translation model with AutoML Translation?
Creating a custom translation model with AutoML Translation involves a series of steps that enable users to train a model specifically tailored to their translation needs. AutoML Translation is a powerful tool provided by Google Cloud AI Platform that leverages machine learning techniques to automate the process of building high-quality translation models. In this answer,
What is the purpose of the Advanced Glossary feature in Translation API?
The Advanced Glossary feature in Google Cloud AI Platform's Translation API serves a important purpose in enhancing the accuracy and quality of machine translation outputs. This feature allows users to provide a custom glossary of terms that are specific to their domain or industry, enabling the translation model to better understand and translate these terms
How does the choice of block size on a persistent disk affect its performance for different use cases?
The choice of block size on a persistent disk can significantly impact its performance for different use cases in the field of Artificial Intelligence (AI) when utilizing Google Cloud Machine Learning (ML) and Google Cloud AI Platform for productive data science. The block size refers to the fixed-size chunks in which data is stored on
What is the difference between AI Platform Optimizer and HyperTune in AI Platform Training?
AI Platform Optimizer and HyperTune are two distinct features offered by Google Cloud AI Platform for optimizing the training of machine learning models. While both aim to improve model performance, they differ in their approaches and functionalities. AI Platform Optimizer is a feature that automatically explores the hyperparameter space to find the best set of