What are the core activities involved in machine learning?
Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. In the context of cloud computing, specifically the Google Cloud Platform (GCP) and its Cloud ML Engine, there are several core activities involved in
What is the difference between machine learning and artificial intelligence (AI) in the context of cloud computing?
In the context of cloud computing, machine learning and artificial intelligence (AI) are two distinct but interconnected concepts. Machine learning refers to the process of enabling computers to learn from data and improve their performance on a specific task without being explicitly programmed. On the other hand, AI is a broader field that encompasses the
What are the benefits of using Cloud ML Engine for training and serving machine learning models?
Cloud ML Engine is a powerful tool provided by Google Cloud Platform (GCP) that offers a range of benefits for training and serving machine learning (ML) models. By leveraging the capabilities of Cloud ML Engine, users can take advantage of a scalable and managed environment that simplifies the process of building, training, and deploying ML
How can you access BigQuery ML?
To access BigQuery ML, you need to follow a series of steps that involve setting up your Google Cloud project, enabling the necessary APIs, creating a BigQuery dataset, and finally, executing SQL queries to train and evaluate machine learning models. First, you need to create a Google Cloud project or use an existing one. This
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, BigQuery ML - machine learning with standard SQL, Examination review
How can you call predictions using a sample row of data on a deployed scikit-learn model on Cloud ML Engine?
To call predictions using a sample row of data on a deployed scikit-learn model on Cloud ML Engine, you need to follow a series of steps. First, ensure that you have a trained scikit-learn model that is ready to be deployed. Scikit-learn is a popular machine learning library in Python that provides various algorithms for
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Scikit-learn models at scale, Examination review
What are the requirements for creating a model and version on Cloud ML Engine for a scikit-learn model?
To create a model and version on Cloud ML Engine for a scikit-learn model, there are certain requirements that need to be fulfilled. Cloud ML Engine is a powerful platform provided by Google Cloud that allows users to train and deploy machine learning models at scale. By leveraging the capabilities of Cloud ML Engine, users