Deep Learning – The Next Evolution in Programming | SlashdotMedia AdOps Asset Management

Deep Learning – The Next Evolution in Programming

Even if you haven’t recognized it, you’ve benefited from deep learning. Deep learning is an advancement essential to cognitive computing, as it partners computers with humans to help businesses automate labor-intensive processes. This progressive approach allows for a more advanced interpretation of data, and the effects are astounding. With the growth of the Internet and the shift to online business, enterprises worldwide struggle everyday to make sense of the staggering amount of unstructured data. Deep learning offers a solution.

Text Analysis: A Crucial Part of the Enterprise Data Initiative

Learn How to Unlock the Hidden Value in Unstructured Data

Data Sheet: Hadoop-as-a-Service

IBM® BigInsights™ on Cloud provides Hadoop-as-a-service on IBM’s SoftLayer® global cloud infrastructure. It offers the performance and security of an on-premises deployment without the cost or complexity of managing your own infrastructure.

BigInsights is an industry standard Apache Hadoop offering that helps enterprises cost-effectively manage and analyze big data.

BigInsights on Cloud provides the following features and benefits:

Managed operations provide 24 x 7 monitoring
IBM Open Platform with current and stable Apache Hadoop components
Dedicated bare metal nodes for enhanced performance, data privacy and security
High value in-Hadoop analytics features, including Big SQL, Big Sheets, Text Analytics, Big R and Machine Learning

DataWorks Solution Brief

IBM DataWorks is a fully managed data preparation and movement service for IBM Cloud Data Services. It enables business analysts, developers, data scientists and engineers to put data to work with a simple yet powerful cloud based interface. DataWorks gives both technical and non-technical users across the enterprise a point and click solution to discover, cleanse, standardize, transform and move data in support of application development and analytic use cases.

Foundational Methodology of Data Science

In the domain of data science, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights. Organizations can then use these insights to take actions that ideally influence future outcomes for the better. The flow of IBM's data science methodology ensures that as data scientists learn more about the data and the modeling, they can return to a previous stage to make adjustments, iterate quickly and provide continuous value to the organization.

Couchbase Impact Report

Version 4.0 of the Couchbase Server NoSQL document database adds multi-dimensional scalability and global secondary indexes, improved cross-datacenter replication, and general availability of N1QL, Couchbase’s SQL-based query language.

Download the whitepaper to learn more!

Moving from Relational to NoSQL: How to Get Started

As enterprises adapt to the digital economy, development and operations teams have to build and maintain web, mobile, and IoT applications faster and at greater scale. At leading companies, NoSQL has become a foundation for success. The Couchbase white paper, “Moving from Relational to NoSQL: How to Get Started,” guides you through:

Choosing the right application for your first NoSQL project
Strategies for modeling relational data as documents
How to access documents within your application
How to migrate data from a relational to a NoSQL database
The basics of operating a NoSQL vs. a relational database

High Performance with Distributed Caching: Key Requirements for Choosing the Right Solution

Many caching solutions are complex, expensive, and lack the key features and capabilities you really need. Learn how to scale your applications while improving performance and cutting costs. This guide, “High Performance with Distributed Caching: Key Requirements for Choosing the Right Solution,” explains:

The key requirements for an effective distributed cache
Advantages and disadvantages of common solutions, including Oracle Coherence and memcached
How Couchbase provides a high-performance, low-cost, easy-to-manage caching solution

Developing Applications with Cross Data Center Replication (XDCR)

Mission critical applications need a database that is always on. While intra-cluster replication helps with server failures giving you high availability, your system still remains at risk from catastrophic failures. You may also need a way to improve the response time for users when they are globally distributed by replicating information closer to them.

In this white paper you'll see:

Overview of XDCR and its main benefits
XDCR architecture, topologies and use cases
How to develop applications with XDCR
Data flow, failover and consistency considerations with XDCR

2015 NoSQL Database Evaluation Guide

While every company has its own specific set of requirements for the NoSQL database technology that best fits its use case(s), there’s a core set of requirements that figure into most evaluations. Those requirements fall into eight categories: Data Access, Performance, Scalability, Availability, Multiple Data Centers, Big Data Integration, Administration, and Mobile. This paper delves deeply into each core requirement followed by a comparison of leading NoSQL databases.

Criteo Case Study

Avec Couchbase Server, Criteo stimule la performance et la montée en charge horizontale de la plate-forme publicitaire numérique.

Market Guide for NoSQL DBMSs

NoSQL DBMSs continue to disrupt modern data management and application development. Traditional vendors are reacting and complicating the decision process for information managers. This Market Guide explores NoSQL developments, vendor reactions and where the market may be heading over the next year.

The NoSQL Tech Comparison Report

In this report, Altoros compares the architecture of three leading NoSQL databases: MongoDB, DataStax Enterprise, and Couchbase Server. The comparison covers infrastructure, administration, deployment, development, and performance and scalability.

In addition, Altoros scores the databases based on twenty (20) evaluation criteria ranging from topology to monitoring to integration. Learn how well Couchbase Server, MongoDB, and Apache Cassandra implement:

Deployment, Fault Tolerance, Persistence
Configuration, Disaster Recovery, Monitoring
Availability, Stability
Integration, Support, Usability
Performance & Scalability

How would you score the databases?

Why NoSQL?

The convergence of social networks, mobile platforms, connected things, big data and cloud infrastructure redefined interactive applications. It redefined the number of users, the data, the infrastructure and a new generation of databases emerged.
Today, successful enterprises are moving beyond relational databases. In the white paper Why NoSQL?, you’ll learn:

How to meet user experience requirements
How to improve developer productivity
How to reduce operational costs
How to improve market agility

Making the Shift from Relational to NoSQL

NoSQL database technology is increasingly chosen as viable alternative to relational databases, particularly for interactive web applications. Developers accustomed to the RDBMS structure and data models need to change their approach when transitioning to NoSQL. In this whitepaper you'll learn:

The main challenges that motivates the need for NoSQL
The differences between relational databases and distributed document-oriented databases
The key steps to perform document modeling in NoSQL databases
How to handle concurrency, scaling and multiple-place updates in a non-relational database