About

PredictionIO is a Machine Learning Engine that is a complete stack for data analysts and scientists to generate analytics engines.

Miri Infotech is launching a product which will configure and publish Predictionio, to produce free implementations of distributed or otherwise scalable and high available resources which are embedded pre-configured tool with ubuntu and ready-to-launch AMI on Amazon EC2 that contains JAVA, predictionio and Spark MLlib

MLlib fits into Spark's APIs and interoperates with NumPy in Python and R libraries. You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it easy to plug into Hadoop workflows.

Spark excels at iterative computation, enabling MLlib to run fast. MLlib contains high-quality algorithms that leverage iteration, and can yield better results than the one-pass approximations sometimes used on MapReduce.

Predictionio works well with open source services like Hadoop, Elasticsearch, Spark, etc. It also works with other DBs in addition to Hbase. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs

It has the following features:

Features

We can deploy a web service with customized templates on our production environment.
After deployed, the service responds to dynamic queries in real-time.
Multiple tasks are evaluated and tuned systematically.
Predictive analytics on real–time data in a batch format is processed easily by this server.
Machine learning model is speeded up and pre-built evaluation measures are systematically processed.
Supports Spark MLLib and OpenNLP.
Custom ML models can also be implemented at an ease and is adapted easily onto the environment.
Data infrastructure management is also supported.

Usage/Deployment Instruction:

Step 1: Open Putty for SSH.

Step 2: Open Putty and Type <instance public IP> at “Host Name” and Type "ubuntu" as user name Password auto taken from PPK file.

Step 3: Use following Linux command to start PredictionIO.

Step 3.1: $source ~/.bashrc

$sudo vi /etc/hosts

Take the Private Ip address from your machine as per the below screenshot and then replace the second line of your command screen with that Private ip address.

Step 4: Start PredictionIO server.

$pio-start-all

$pio status

As we can see in the above messages, our system is ready to use for implementing an engine template and predicting data.

For Spark:

Step 1: start the spark shell by the following command:

$spark-shell

Step 2: open the browser and hit http://<instance-ip>:4040

(Optional)

Example: Implementing a demo Engine Template

Step 1: Run the following commands:

$cd MyEComRecomm

$pio app new <your-app-name>

After entring the above command, the output contains the access key which will be used to authenticate when sending the input data to the event server.

Step2: Run the Python script to add the sample data to the event server.

$python data/import_eventserver.py --access_key <Access Key>

Note: (Replace with own generated access key)

$curl -i -X GET "http://localhost:7070/events.json?accessKey=<Access Key>"

$nano engine.json

edit "appName": "<your-app-name>" //As your application Name set in step 1.

Save and exit.

$pio build --verbose

$pio train

$pio deploy

You can verify the status of the engine by going to http://<public ip>:8000

All your queries are important to us. Please feel free to connect.

24X7 support provided for all the customers.

We are happy to help you.

Submit your Query: http://www.miritech.com/ContactUs.aspx

Contact Numbers:

(510) 298-5936
(510) 870-8668

Contact E-mail:

support@miritech.com

Other Useful Links:

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Product Demo:

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

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

1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7.When you are ready, choose Review and Launch.

8. 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.

9. 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

1What can I do with Amazon EC2?

Amazon EC2 enables “compute” in the cloud. Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction. It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment. Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change. Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use.
2Do I need one Elastic IP address for every instance that I have running?

No. You do not need an Elastic IP address for all your instances. By default, every instance comes with a private IP address and an internet routable public IP address. The private address is associated exclusively with the instance and is only returned to Amazon EC2 when the instance is stopped or terminated. The public address is associated exclusively with the instance until it is stopped, terminated or replaced with an Elastic IP address. These IP addresses should be adequate for many applications where you do not need a long lived internet routable end point. Compute clusters, web crawling, and backend services are all examples of applications that typically do not require Elastic IP addresses.
3What can I do with Amazon S3?

Amazon S3 provides a simple web service interface that you can use to store and retrieve any amount of data, at any time, from anywhere on the web. Using this web service, you can easily build applications that make use of Internet storage. Since Amazon S3 is highly scalable and you only pay for what you use, you can start small and grow your application as you wish, with no compromise on performance or reliability.

Amazon S3 is also designed to be highly flexible. Store any type and amount of data that you want; read the same piece of data a million times or only for emergency disaster recovery; build a simple FTP application, or a sophisticated web application such as the Amazon.com retail web site. Amazon S3 frees developers to focus on innovation instead of figuring out how to store their data
4How do I choose the right configuration parameters for my DB Instance(s)?

By default, Amazon RDS chooses the optimal configuration parameters for your DB Instance taking into account the instance class and storage capacity. However, if you want to change them, you can do so using the AWS Management Console, the Amazon RDS APIs, or the AWS Command Line Interface. Please note that changing configuration parameters from recommended values can have unintended effects, ranging from degraded performance to system crashes, and should only be attempted by advanced users who wish to assume these risks.
5How secure is my data in Amazon S3?

Amazon S3 is secure by default. Upon creation, only the resource owners have access to Amazon S3 resources they create. Amazon S3 supports user authentication to control access to data. You can use access control mechanisms such as bucket policies and Access Control Lists (ACLs) to selectively grant permissions to users and groups of users. The Amazon S3 console highlights your publicly accessible buckets, indicates the source of public accessibility, and also warns you if changes to your bucket policies or bucket ACLs would make your bucket publicly accessible.

You can securely upload/download your data to Amazon S3 via SSL endpoints using the HTTPS protocol. If you need extra security you can use the Server-Side Encryption (SSE) option to encrypt data stored at rest. You can configure your Amazon S3 buckets to automatically encrypt objects before storing them if the incoming storage requests do not have any encryption information. Alternatively, you can use your own encryption libraries to encrypt data before storing it in Amazon S3.
6How do I create a DB instance?

DB instances are simple to create, using either the AWS Management Console, Amazon RDS APIs, or AWS Command Line Interface. To launch a DB instance using the AWS Management Console, click "RDS," then the Launch DB Instance button on the Instances tab. From there, you can specify the parameters for your DB instance including DB engine and version, license model, instance type, storage type and amount, and master user credentials.

You also have the ability to change your DB instance’s backup retention policy, preferred backup window, and scheduled maintenance window. Alternatively, you can create your DB instance using the CreateDBInstance API or create-db-instance command.
7How many databases or schemas can I run within a DB instance?
- RDS for Amazon Aurora: No limit imposed by software
- RDS for MySQL: No limit imposed by software
- RDS for MariaDB: No limit imposed by software
- RDS for Oracle: 1 database per instance; no limit on number of schemas per database imposed by software
- RDS for SQL Server: 30 databases per instance
- RDS for PostgreSQL: No limit imposed by software
8Is there an easy way to migrate data from Hadoop into a relational database?

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.
9Are Intelligent Assistants the only application of AI for customer care, or are there other ways that AI technologies can impact the contact center?

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.
10What are the popular machine learning algorithms in use today?
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

PredictionIO

About

Submit Your Request

1What can I do with Amazon EC2?

2Do I need one Elastic IP address for every instance that I have running?

3What can I do with Amazon S3?

4How do I choose the right configuration parameters for my DB Instance(s)?

5How secure is my data in Amazon S3?

6How do I create a DB instance?

7How many databases or schemas can I run within a DB instance?

8Is there an easy way to migrate data from Hadoop into a relational database?

9Are Intelligent Assistants the only application of AI for customer care, or are there other ways that AI technologies can impact the contact center?

10What are the popular machine learning algorithms in use today?

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