All about H2O

All about H2O

The aim of behind developing H2O is to make machine learning easily accessible. H2O is a mountain view, California based company run by It provides users with machine learning models which can be integrated for the sake of increased productivity. It's and open source software whose programming is done with the help of Java and rest API. Along with this H2O has interface with JSON, Scala, Python, R, Java, JavaScript and a built-in interface. H2O provides AutoML functionality and uses statistical algorithm to process data. H2O effectively retrieves and identifies the data at a faster rate, helps in predicting model. Importing of data and information is quick, adaptable and distribution is easy. All this is because of high level framework of H2O. H2O.3 is the latest version of H2O. It is a package of python which can be downloaded from PyPI and R package can be installed in system from CRAN. To construct program and execute H2O, a JDK of 64 bit for Java and 64 bit for R or Python package is required. There’s a need of establishing connection between the cluster of H2O and Python end-user prior to the start of every Python discussions. An example of H2O local cluster is import H2O ; h2o.init(). It’s important to remove bugs. About 12000 organisations and 129000 data centres are working behind the scenes for H2O. It has shown tremendous growth rate of 330% in last couple of years. Companies and firms like IBM, MS, NVIDIA, Map R, Splunk, Data bricks, Cloudera, Anaconda and few more are its partner. H2O provides all these firms with its easily accessible machine learning services and eliminates the need of employing experts in this domain for company resulting in cost reduction.

H2O sparkling water: It's a combination of potentials of spark with features of H2O like speed and structured algorithms of machine learning. This combo is used for handling and processing of large amount of data in addition to transferring from H2O to spark or vice versa.

Deep water: Integration of H2O with tensorflow, Caffe and MX Net for GPU dependent work to be performed.

H2O4GPU: GPU helps in constructing an advanced machine learning models for H2O with the aid of accelerated API in R and Python. The duo is open source thus, freely available.

H2O driverless AI: The merging of H2O with AI results in an automated machine learning framework. It enables assembling, deployment and model tuning with greater productivity and ease. affirms that driverless AI introduces the quality of Kaggle Grandmaster box. Its commercially licensed too, therefore is trustworthy. AutoML in H2O makes the training and application process automatic from end to end.

Some of the features of H2O are as follows:

The data parser of H2O incorporates a built in intelligence which can predict the framework of input data from different sources. It also enables predictive analytics.

H2O makes an alliance with Hadoop Tools and Spark to cope up with big data. Horton works HDP, Cloudera CDH, Map R4.X and IBM platforms are some of the distributions from Hadoop.

Conda and H2O merge together to form and open source management system. It’s mostly used when faster installing, updation and running of data is required. Both Anaconda and Conda can easily be accessed on Anaconda clouds where in each information can be shared. Until there is no need of running H2O on anaconda cloud, Conda is not required.

H2O Collaborations

Cisco : A statistical model called as P2B uses H2O in its development. It predicts the probability of occurrence and requirement like, money that a firm would spend to buy certain products and within how much time it would achieve its target. These features are very much useful in marketing and economics indicating surveys, etc.

Capital one: H2O’s ML and data analytics resources being open source satisfies the requirement of systems and operations group. They use H2O in building banking app.

Equifax: A model called Ignite was proposed by Equifax in combination with H2O. Ignite is an advanced and unique portfolio of information and provides analytical solution.

Kaiser Permanente : H2O is used in developing models that are used in ICU transfer and allotment to patients who are in urgent requirement of the ICU beds. This technology has helped in decreasing the death rates in hospital by 2-5 times.

Google APIs for translations are not exact as models trained when compared to their own database. The spark H2O popularly called sparkling water is used as cluster in upstream codes to solve this problem. In the similar way H2O allows training of a variety of models. H2O deep learning estimators are required to be imported to use such models. H20 estimators deep learning module provides with instance for the same model creation. Cross validation in H2O is a core methodology utilised in machine learning which involves splitting up of data in parts to train them individually while, leaving one of the set untrained to test later on and compare. The same process is repeated but after testing step, the left data set is also included in next iterations.

H2O has features which takes in account in memory computation for machine learning algorithms and computational ability of system. Therefore H2O provides an enterprise support to end users. Some other applications of H2O are digital marketing, fraud identification, claim and copyright management, predictive analytics and many more.

More than 18000 firms and organisations use H2O throughout the globe and reason being features like AutoML, R and Python which it supports. H2O has great potential to develop the data science field for sake of betterment of computing and networking. It incorporates everything from AI, R, Python to automatic ML. With each passing year H2O is reaching closer to its mission of democratising AI for all.