H2O- FOR FASTER DATA COMPUTATIONS

·       H2O is an open source machine learning platform where companies can build models on large data sets (no sampling needed) and achieve accurate predictions. It is incredibly fast, scalable and easy to implement at any level. In simple words, they provide a GUI driven platform to companies for doing faster data computations. Currently, the platform supports advanced & basic level algorithms such as deep learning, boosting, bagging, naive bayes, principal component analysis, time series, k-means, generalized linear models.

In addition, H2O has released APIs for R, Python, Spark, Hadoop users so that people like us can use it to build models at individual level. It’s free to use and instigates faster computation.

Why is H2O faster?

H2O has a clean and clear feature of directly connecting the tool (R or Python) with your machine’s CPU. This way we get to channelize more memory, processing power to the tool for making faster computations. This will allow computations to take place at 100% CPU capacity. It can also relate to clusters at cloud platforms(AWS)doing computations. To use the Amazon Web Services (AWS) S3 storage solution, you will need to pass your S3 access credentials to H2O. This will allow you to access your data on S3.

Along with, it uses in-memory compression to handle large data sets even with a small cluster. It also includes provisions to implement parallel distributed network training.

How H2O works?

H2O’s core code is written in Java. Inside H2O, a Distributed Key/Value store is used to access and reference data, models, objects, etc., across all nodes and machines. The algorithms are implemented on top of H2O’s distributed Map/Reduce framework and utilize the Java Fork/Join framework for multi-threading. The data is read in parallel and is distributed across the cluster and stored in memory in a columnar format in a compressed way. H2O’s data parser has built-in intelligence to guess the schema of the incoming data set and supports data ingest from multiple sources in various formats. 

H2O Usage:

Download H2O package in R and initialize H2O using command H2O.init().

Further H2O can be used in one of the following ways.

1) Command line interface by typing commands to use H2O.

2) Web User Interface

Algorithms provided by H2O 

·       Deep Learning

·       Distributed Random Forest

·       Gradient Boosting Method

·       Generalized Linear Modeling

·       K-Means

·       Naive Bayes

·       Principal Component Analysis.

Advantages of H2O

·       High-Speed Processing

·       Scale to big data without sampling.

·       Provides web interface. No need for the user to learn commands.

·       Provides Machine Learning Algorithms to analyze the data and used for data prediction.