Big Data & Data Science

Leveraging insights from research and industry expertise, we provide powerful machine learning solutions for the B2B & B2C markets. We are the best Big Data & Data Science solution provider serving clients in different sectors and regions.

DEEP LEARNING

One of the challenges in using traditional machine learning techniques is the choice of features. For humans to analyse data and recommend a solution, it generally takes few days. Our approach to such situations is to use an unsupervised technique. We are able to make the machine derive the best features and learn by itself, take feedback and then learn again. Some of the deep learning networks we have used in the past include Restricted Boltzmann Machines, Deep Belief Networks, Convolutional Networks etc. We have successfully used these machines to solve problems such as Image classification, Video classification, Speech recognition, handwriting recognition etc.

MACHINE LEARNING

We develop systems that solve your complex business problems using our machine learning techniques. With the strong knowledge of algorithms, math geniuses and data engineers put their brains together in identifying the solution and build them to make it awesome. From recommendation engines to spam prediction systems to social media analytics, we have powerful engines built upon ML techniques.

CLOUD BASED ANALYTICS

We offer a slew of cloud-based solutions for small, medium and large enterprises. With zero investment in infrastructure, organizations rely on this robust platform to meet their need for rapid scalability, faster implementation and accessibility anytime-anywhere. Majority of our projects follow a SaaS model whereas we have also executed few PaaS services with elastic infrastructure.

NATURAL LANGUAGE PROCESSING

Our data experts are the best in Natural language processing. We build systems that deals with analyzing, understanding & identifying data from languages that humans use. We have built NLP systems for Advertising, Healthcare & Retail domain.

Natural Language Processing

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human-computer interaction.

OpenNLP

The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services.

Machine Learning

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions.

Weka

Waikato Environment for Knowledge Analysis (Weka) is a suite of machine learning software written in Java. Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling. Weka supports several data mining tasks, more specifically data preprocessing, clustering, classification, regression, visualization and feature selection.

Data Mining

Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Apache Nutch

Apache Nutch is an open source Java implementation of a search engine. It provides all of the tools you need to run your own search engine.Nutch is open source so we can access ranking algorithms. Nutch can add search to information of heterogeneous type or can use plugins to add additional functionalities.

Large Data Store

The methodology selected to store big data should reflect the application and its usage patterns. Traditional data warehousing operations mined relatively homogenous data sets, often supported by fairly monolithic storage infrastructures in a way that today would be considered less than optimal in terms of the ability to add processing or storage capacity. By contrast, Large data stores processe on huge amount and hetrogenious datas.

Hadoop

Hadoop software library is a framework that allows for the distributed processing of large data sets.It is designed to scale up from single servers to thousands of machines, each offering local computation and storage, Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer.It can store data in raw or any of the serialized formats like Avro, SequenceFile etc.

In Memory Data Storage

An in-memory database (IMDB; also main memory database system or MMDB or memory resident database) is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism.

WhiteDB

WhiteDB is a lightweight database which is known for its speed and portability across ecosystems,it operates fully in main memory. Disk is used only for dumping/restoring database and logging. Data is persistently kept in the shared memory area and is available simultaneously to all processes.

Distributed Processing

Distributed processing is a phrase used to refer to a variety of computer systems that use more than one computer (or processor) to run an application. This includes parallel processing in which a single computer uses more than one CPU to execute programs.

Apache Giraph

Apache Giraph is an iterative graph processing system built for high scalability. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale.

Graph Data Store

Database that embraces relationships as a core aspect of its data model is able to store, process, and query connections efficiently. While other databases compute relationships expensively at query time, a graph database stores connections as first class citizens, readily available for any “join-like” navigation operation. Accessing those already persistent connections is an efficient, constant-time operation and allows you to quickly traverse millions of connections per second per core.

Titan

Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.Titan has the ability to use Cassandra/HBase etc for storage and ElasticSearch for indexing. .It is basically a scalable graph processing over big data processing systems.

Data Pipelines

A pipeline is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion; in that case, some amount of buffer storage is often inserted between elements.

Apache Kafka

Apache Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization.

WHOM WE SERVE

Healthcare

Retail

Insurance

Automobile

Advertising

Media & Entertainment

Energy & Utilities

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