How do models relate to versions in Google Cloud Machine Learning Engine (renamed to Google Cloud AI Platform)?
Google Cloud AI Platform, formerly known as Cloud Machine Learning Engine, is a robust service designed for training and deploying machine learning models at scale. Within this platform, the concepts of "models" and "versions" are pivotal, serving as the fundamental units for managing machine learning workflows. Models in Google Cloud AI Platform A "model" in
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Google Cloud AI Platform, AI Platform training with built-in algorithms
Can uploading of small to medium datasets be done with the gsutil command-line tool through the network?
The gsutil command-line tool, provided by Google Cloud Platform, offers a convenient and efficient way to upload small to medium datasets through the network. With gsutil, users can interact with Google Cloud Storage, a scalable and durable object storage service, to store and retrieve data. To upload datasets using gsutil, you need to have the
What features are available for viewing job details and resource utilization in Google Cloud AI Platform?
In Google Cloud AI Platform, there are several features available for viewing job details and resource utilization. These features provide users with valuable insights into the progress and efficiency of their machine learning training jobs. By monitoring job details and resource utilization, users can optimize their training workflows and make informed decisions to improve the
What is HyperTune and how can it be used in AI Platform Training with built-in algorithms?
HyperTune is a powerful feature offered by Google Cloud AI Platform that enhances the training process of machine learning models by automating the hyperparameter tuning process. Hyperparameters are parameters that are not learned by the model during training but are set by the user before the training process begins. These parameters significantly impact the performance
What options are available for specifying validation and test data in AI Platform Training with built-in algorithms?
When using Google Cloud AI Platform for training machine learning models, there are several options available for specifying validation and test data when using the built-in algorithms. These options provide flexibility and control over the training process, allowing users to evaluate the performance of their models and ensure their effectiveness before deployment. One option is
How should the input data be formatted for AI Platform Training with built-in algorithms?
To properly format input data for AI Platform Training with built-in algorithms, it is essential to follow specific guidelines to ensure accurate and efficient model training. AI Platform provides a variety of built-in algorithms, such as XGBoost, DNN, and Linear Learner, each with its own requirements for data formatting. In this answer, we will discuss
What are the three structured data algorithms currently available in AI Platform Training with built-in algorithms?
The AI Platform Training, offered by Google Cloud, provides a range of built-in algorithms for training machine learning models. These algorithms are designed to handle structured data and are specifically tailored to address various tasks in the field of artificial intelligence. In this answer, we will explore the three structured data algorithms currently available in