Get Big data insights faster with GraphLab
Get Big data insights faster with GraphLab : GraphLab Create, is a software platform that brings large scale machine learning capabilities including predictive analytics to data scientists at any organization. GraphLab Create is available in three pricing editions which are meant to map to the typical predictive application development journey: Discover, Develop, Deploy. GraphLab also support Enterprise-wide programs.Dato is the company behind GraphLab Create and it was started at Carnegie Mellon in 2009 as an open source project under the guidance of Carlos Guestrin, PhD. The software was initially intended for applying large scale machine learning to graph analysis. The functionality has since been much augmented to include tables, text, images and is now in broad use to make recommendations, detect fraud, score marketing content and generally deliver predictive analytics at many notable e-tailers, service providers and Fortune 5000 firms. Organizations across industries including retail, media, insurance and healthcare understand the significant positive impact that big data and predictive applications can have on profitability, customer satisfaction and competitive position. However, to date, these intelligence infused applications have been time consuming to build and challenging to scale for production, requiring a variety of disparate tools and skilled data scientists.
GraphLab Create is a machine learning platform built for developers and data scientists with functional programming skills and some basic understanding of data science. It allows them to easily prototype and scale their ideas from inspiration to production. With GraphLab Create, savvy developers can help their companies unlock the value of their data through predictive applications and machine learning. GraphLab Create makes deployment and integration easy by turning machine learning models into predictive services consumable by front-end web applications. Example services include recommenders, fraud detectors or customer churn predictors. Developers and data scientists are able to quickly deploy and easily integrate with other applications. Businesses including Adobe, Zillow and Crosswise are a few of the companies already using GraphLab Create to drive business and enhance customer experience. Data science training program Zipfian Academy has also incorporated the software into its curriculum so data scientists can take advantage of its speed, ease of use, and ability to handle large data sets.
GraphLab Create scalably unifies tables and graphs with SFrame, SArray, and Graph. SFrame is a tabular on-disk data structure that will scale to TBs and bigger. SArray is a columnar representation, used by SFrames, analogous to a pandas Series object and SGraph is an ideal structure for capturing sparse relationships between things. SGraphs are fundamental for efficient computation of many machine learning models.
The features available in the GraphLab Create platform also includes Predictive Services, Deep Learning, Boosted Trees, Visualization.
Companies can build predictive applications quickly, easily, and at scale. Predictive service deployments are scalable, fault-tolerant, and high performing, enabling easy integration with front-end applications. 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.
These models are ideal for automatic learning of salient features, without human supervision, from data such as images. Combined with GraphLab Create image analysis tools, the Deep Learning package enables accurate and in-depth understanding of images and videos. The GraphLab Create image analysis package makes quick work of importing and preprocessing millions of images as well as numeric data. It is built on the latest architectures including Convolution Layer, Max, Sum, Average Pooling and Dropout. The available API allows for extensibility in building user custom neural networks. Applications include image classification, object detection and image similarity.
With this feature, GraphLab adds support for this popular class of algorithms for robust and accurate regression and classification tasks. With an out-of-core implementation, Boosted Trees in GraphLab Create can easily scale up to large datasets that do not fit into memory.
New dashboards allow users to visualize the status and health of offline jobs deployed in various environments including local, Hadoop Clusters and EC2. Also part of GraphLab Canvas is the visualization of GraphLab SFrames and SGraphs, enabling users to explore tables, graphs, text and images, in a single interactive environment making feature engineering more efficient.
Auto-tuning Toolkits API
The auto-tuning API automatically selects a model for a given business use making it easier to get started for those new to machine learning.
Also provides integrations with SPARK, Apache Avro, and Hadoop. GraphLab users can leverage data available in SPARK to build sophisticated and scalable machine learning solutions. GraphLab Create can directly ingest and offer insight from log data in Apache Avro, the commonly used remote procedure and data serialization framework used in Hadoop. GraphLab is integrated with Hadoop, enabling GraphLab customers who want to leverage Hadoop Distributed File System (HDFS) data sources to do so with minimal effort. GraphLab Hadoop partnerships include Cloudera and Hortonworks.
GraphLab Create editions:
• Discover edition offers a developer’s license with community forum support.
• Develop edition offers a developer’s license with responsive technical support from GraphLab experts.
• Deploy edition offers a license with production-grade support for a team of developers and a new, data-savvy program to help fast-track deployments.
• GraphLab is also working with a number of customers for Enterprise-wide use of GraphLab Create.
GraphLab is open sourced under the Apache License 2.0, and is a graph-based, high performance, distributed computation framework written in C++. The GraphLab API built on top of standard cluster and cloud technologies. Inter process communication is accomplished over TCP-IP and MPI is used to launch and manage GraphLab programs.
GraphChi is disk based large scale graph computation. Programs for GraphChi are written in similar vertex-centric model as GraphLab. GraphChi runs vertex-centric programs asynchronously , and in parallel. GraphChi also supports streaming graph updates and changing the graph structure while computing.
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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.