A large volume of collected data is calculated, analyzed, summarized, and organized by using appropriate statistics and machine learning methods to maximize data value and make full use of data. Data analysis aims to centrally process and extract information from a large volume of various data to summarize internal patterns of the study objects.
It means a process to automatically search for hidden and special information from a large volume of data. Data mining is relevant to computer science. It uses methods such as statistics, online analysis and processing, information retrieval, machine learning, and model identification to search for hidden and special information.
Machine Learning Service (MLS) is an analysis platform service that helps you record patterns in data to construct a machine learning model. You can use this model to process new data and make predictions on service applications.
A machine learning service instance (MLS instance) consists of two ECSs in active/standby mode. MLS instances must be created for using MLS. You can create multiple MLS instances at the same time as well as manage and access them.
Models use data for prediction. Models in the system are stored and managed in the Predictive Model Markup Language (PMML) format. You can train the models on the MLS instance operation interface and use the Save PMML node to store them in the PMML format. You can also manage the published PMML models by using the MLS model management function.
It means the process of delivering computing tasks, which are steps for a model to process data, to the system for execution.
Model construction is a process of selecting data and algorithms and executing algorithms to generate a model.
Models are evaluated using metrics such as precision and recall rates. Metric calculation is called model evaluation.
It means the process for the existing models to analyze data and output results for each data sample.
Model information is presented in graphics.
A node is a logical running unit, standing for a data processing substep. MLS encapsulates various data processing steps (data loading, data pre-processing, and machine learning algorithms) into different nodes, masking programming details. You can drag node icons, connect nodes, and modify node properties to flexibly import, export, convert, and analyze data.
A notebook is a development environment supporting multiple programming languages and provides data modeling capabilities based on the interactive programming mode. Notebooks are intended for data analysts who have related modeling language skills. You can edit, debug, and run code in a notebook as well as smoothly migrate offline code to the notebook.
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