What is the meaning of the term serverless prediction at scale?
The term "serverless prediction at scale" within the context of TensorBoard and Google Cloud Machine Learning refers to the deployment of machine learning models in a way that abstracts away the need for the user to manage the underlying infrastructure. This approach leverages cloud services that automatically scale to handle varying levels of demand, thereby
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Serverless predictions at scale
What will hapen if the test sample is 90% while evaluation or predictive sample is 10%?
In the realm of machine learning, particularly when utilizing frameworks such as Google Cloud Machine Learning, the division of datasets into training, validation, and testing subsets is a fundamental step. This division is critical for the development of robust and generalizable predictive models. The specific case where the test sample constitutes 90% of the data
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
What is an evaluation metric?
An evaluation metric in the field of artificial intelligence (AI) and machine learning (ML) is a quantitative measure used to assess the performance of a machine learning model. These metrics are important as they provide a standardized method to evaluate the effectiveness, efficiency, and accuracy of the model in making predictions or classifications based on
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
What are algorithm’s hyperparameters?
In the field of machine learning, particularly within the context of Artificial Intelligence (AI) and cloud-based platforms such as Google Cloud Machine Learning, hyperparameters play a critical role in the performance and efficiency of algorithms. Hyperparameters are external configurations set before the training process begins, which govern the behavior of the learning algorithm and directly
How to best summarize what is TensorFlow?
TensorFlow is an open-source machine learning framework developed by the Google Brain team. It is designed to facilitate the development and deployment of machine learning models, particularly those involving deep learning. TensorFlow allows developers and researchers to create computational graphs, which are structures that describe how data flows through a series of operations, or nodes.
What is the difference between hyperparameters and model parameters?
In the realm of machine learning, distinguishing between hyperparameters and model parameters is important for understanding how models are trained and optimized. Both types of parameters play distinct roles in the model development process, and their correct tuning is essential for the efficacy and performance of a machine learning model. Model parameters are the internal
What does hyperparameter tuning mean?
Hyperparameter tuning is a critical process in the field of machine learning, particularly when utilizing platforms such as Google Cloud Machine Learning. In the context of machine learning, hyperparameters are parameters whose values are set before the learning process begins. These parameters control the behavior of the learning algorithm and have a significant impact on
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
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
What is text to speech (TTS) and how it works with AI?
Text-to-speech (TTS) is a technology that converts text into spoken language. In the context of Artificial Intelligence and Google Cloud Machine Learning, TTS plays a important role in enhancing user experience and accessibility. By leveraging machine learning algorithms, TTS systems can generate human-like speech from written text, enabling applications to communicate with users through spoken
What are the limitations in working with large datasets in machine learning?
When dealing with large datasets in machine learning, there are several limitations that need to be considered to ensure the efficiency and effectiveness of the models being developed. These limitations can arise from various aspects such as computational resources, memory constraints, data quality, and model complexity. One of the primary limitations of installing large datasets
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, GCP BigQuery and open datasets