GraphLab Create is a machine learning platform to build intelligent, predictive application involving cleaning the data, developing features, training a model, and creating and maintaining a predictive service.
Predictive Analytics Software
GraphLab Create is a machine learning platform to build intelligent, predictive application involving cleaning the data, developing features, training a model, and creating and maintaining a predictive service. These intelligent applications provide predictions for use cases including recommenders, sentiment analysis, fraud detection, churn prediction and ad targeting. Trained models can be deployed on Amazon Elastic Compute Cloud (EC2) and monitored through Amazon CloudWatch. They can be queried in real-time via a RESTful API and the entire deployment pipeline is seen through a visual dashboard. The time from prototyping to production is dramatically reduced for GraphLab Create users.
Dato is also offering an open-source release of GraphLab Create’s core code. Included in this version is the source for the SFrame and SGraph, along with many machine learning models, such as triangle counting, pagerank and more. Using this code, it is easy to build a new machine learning toolkit or a connector from the Dato SFrame to a data store.Each incorporates automatic feature engineering, model selection, and machine learning visualizations specific to the application. A recommender system allows you to provide personalized recommendations to users. With this toolkit, you can train a model based on past interaction data and use that model to make recommendations. GraphLab Create also contains sentiment analysis. Data scientists are often faced with data sets that contain text, and must employ natural language processing (NLP) techniques in order to make it useful. Sentiment analysis refers to the use of NLP techniques to extract subjective information such as the polarity of the text, e.g., whether or not the author is speaking positively or negatively about some topic. Churn prediction is the task of identifying whether users are likely to stop using a service, product, or website. With this toolkit, you can start with raw (or processed) usage metrics and accurately forecast the probability that a given customer will churn. Data matching is the identification and aggregation of data records that correspond to the same real-world entity. Often data matching problems arise when aggregating datasets from different sources, but the field of data matching encompasses several different tasks that have quite different data contexts. The GraphLab Create data matching toolkit provides four tools to help you quickly accomplish the most common data matching tasks. Record linker is the most straightforward data matching task: linking structured query records to a fixed reference set, also in tabular form.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.