H2O

BIG-DATA ANALYSISpythonLaravelH2O
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About

An AWS product H2O: predicts fraud and stops it in its tracks powered by Miri Infotech. H2O is open-source software for big-data analysis. It is produced by the start-up H2O.ai (formerly 0xdata), which launched in 2011 in Silicon Valley. The speed and flexibility of H2O allow users to fit hundreds or thousands of potential models as part of discovering patterns in data. With H2O, users can throw models at data to find usable information, allowing H2O to discover patterns. Using H2O, Cisco estimates each month 20 thousand models of its customers’ propensities to buy.

We are configuring and publishing H2O embedded pre-configured tool with Python and ready-to-launch AMI on Amazon EC2 that contains Python and H2O.

H2O’s mathematical core is developed with the leadership of Arno Candel; after H2O was rated as the best “open-source Java machine learning project” by GitHub’s programming members, Candel was named to the first class of “Big Data All Stars” by Fortune in 2014.The firm’s scientific advisors are experts on statistical learning theory and mathematical optimization.

The H2O software runs can be called from the statistical package R and other environments. It is used for exploring and analyzing datasets held in cloud computing systems and in the Apache Hadoop Distributed File System as well as in the conventional operating-systems Linux, macOS, and Microsoft Windows. The H2O software is written in Java, Python, and R. Its graphical-user interface is compatible with four popular browsers: Chrome, Safari, Firefox, and Internet Explorer.

Features:

  • Best of Breed Open Source Technology: Enjoy the freedom that comes with big data science powered by open source technology. H2O was written from scratch in Java and seamlessly integrates with the most popular open source products like Apache Hadoop® and Spark™ to give customers the flexibility to solve their most challenging data problems.
  • Easy-to-use WebUI and Familiar Interfaces: Set up and get started quickly using either H2O’s intuitive web-based Flow graphical user interface or familiar programming environments like R, Python, Java, Scala, JSON, and through our powerful APIs. Models can be visually inspected during training, which is unique to H2O.
  • Data Agnostic Support for all Common Database and File Types: Easily explore and model big data from within Microsoft Excel, R Studio, Tableau and more. Connect to data from HDFS, S3, SQL and NoSQL data sources. Install and deploy anywhere, in the cloud, on premise, on workstations, servers or clusters.
  • Massively Scalable Big Data Analysis: Train a model on complete data sets, not just small samples, and iterate and develop models in real-time with H2O’s rapid in-memory distributed parallel processing.
  • Real-time Data Scoring: Rapidly deploy models to production via plain-old Java objects (POJO) or model-optimized Java objects (MOJO). Score new data against models for accurate predictions in any environment. Enjoy faster scoring and better predictions than any other technology.
  • Scalability + Speed: Fine-Grain Distributed Processing on Big Data at Speeds Up to 100x Faster.
  • In-Memory Processing Responsiveness: With H2O, your organization can harness the responsiveness of highly optimized in-memory processing, so you can operationalize many more models and gain real-time intelligence in business transactions and interactions.

You can subscribe to H2O, an AWS Marketplace product and launch an instance from the H2O product’s AMI using the Amazon EC2 launch wizard.

To launch an instance from the AWS Marketplace using the launch wizard

  • Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/
  • From the Amazon EC2 dashboard, choose Launch Instance. On the Choose an Amazon Machine Image (AMI) page, choose the AWS Marketplace category on the left. Find a suitable AMI by browsing the categories, or using the search functionality. Choose Select to choose your product.
  • A dialog displays an overview of the product you’ve selected. You can view the pricing information, as well as any other information that the vendor has provided. When you’re ready, choose Continue.
  • On the Choose an Instance Type page, select the hardware configuration and size of the instance to launch. When you’re done, choose Next: Configure Instance Details.
  • On the next pages of the wizard, you can configure your instance, add storage, and add tags. For more information about the different options you can configure, see Launching an Instance. Choose Next until you reach the Configure Security Group page.
  • The wizard creates a new security group according to the vendor’s specifications for the product. The security group may include rules that allow all IP addresses (0.0.0.0/0) access on SSH (port 22) on Linux or RDP (port 3389) on Windows. We recommend that you adjust these rules to allow only a specific address or range of addresses to access your instance over those ports
  • When you are ready, choose Review and Launch.
  • On the Review Instance Launch page, check the details of the AMI from which you’re about to launch the instance, as well as the other configuration details you set up in the wizard. When you’re ready, choose Launch to select or create a key pair, and launch your instance.
  • Depending on the product you’ve subscribed to, the instance may take a few minutes or more to launch. You are first subscribed to the product before your instance can launch. If there are any problems with your credit card details, you will be asked to update your account details. When the launch confirmation page displays.

Usage/Deployment Instruction

Note: Please open the following Security Ports in the instance: 5601, 9200, 54323, 9093, 2181, 9092, 5902, 5901, 3000, 8091, 54321, 4040, 8787, 8080, 8088

Step 1: Connect with putty

  • Open Putty >> <instance ip address> >>
  • Open SSH From Left side.
  • Click On Auth
  • Click on Browse Button from Right Side.
  • Select your ppk file.
  • At the end clicks on open.
  • Now your putty is open.

Step 2: Type user name and press enter

Step 3: Run command sudo su

Step 4: Run Command python

Step 5: Run Command import h2o

Step 6: Run Command h2o.init()

Step 7: Hit url on browser http://<public ip of instance>:54321/

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    The Apache Hadoop software library allows for the distributed processing of large data sets across clusters of computers using a simple programming model. The software library is designed to scale from single servers to thousands of machines; each server using local computation and storage. Instead of relying on hardware to deliver high-availability, the library itself handles failures at the application layer. As a result, the impact of failures is minimized by delivering a highly-available service on top of a cluster of computers.

    Hadoop, as a scalable system for parallel data processing, is useful for analyzing large data sets. Examples are search algorithms, market risk analysis, data mining on online retail data, and analytics on user behavior data.

    Add the words “information security” (or “cybersecurity” if you like) before the term “data sets” in the definition above. Security and IT operations tools spit out an avalanche of data like logs, events, packets, flow data, asset data, configuration data, and assortment of other things on a daily basis. Security professionals need to be able to access and analyze this data in real-time in order to mitigate risk, detect incidents, and respond to breaches. These tasks have come to the point where they are “difficult to process using on-hand data management tools or traditional (security) data processing applications.”

    The Hadoop JDBC driver can be used to pull data out of Hadoop and then use the DataDirect JDBC Driver to bulk load the data into Oracle, DB2, SQL Server, Sybase, and other relational databases.

    Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. One that I think is particularly interesting is the application of AI to directly support — rather than replace — contact center agents. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond. AI technologies also have an important role in analytics. They can be used to provide an overview of activities within a call center, in addition to providing valuable business insights from customer activity.

    There are many machine learning algorithms in use today, but the most popular ones are:

    • Decision Trees
    • Naive Bayes Classification
    • Ordinary Least Squares Regression
    • Logistic Regression
    • Support vector machines
    • Ensemble Methods
    • Clustering Algorithms
    • Principal Component Analysis
    • Singular Value Decomposition
    • Independent Component Analysis

    Highlights

    • icon

      Real-time Data Scoring and Healthcare

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      Predicts frauds and stops it in its tracks; increases customer lifetime value

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      Reduces risk with pattern-based AML; empowers business transfor

    Application Installed

    • icon H2O
    • icon apache
    • icon linux
    • icon hadoop
    • icon python