Leader in Data Science and Machine Learning Platforms

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KNIME Analytics Platform is a leading open solution for data-driven innovation, designed for discovering the potential hidden in data, mining for fresh insights, or predicting new futures. Organisations can take their collaboration, productivity, and performance to the next level with a robust range of commercial extensions to this open source platform.

KNIME allows users to visually create data flows (or pipelines), selectively execute some or all analysis steps, and later inspect the results, models, and interactive views. It is an open source data analytics-, reporting-, and integration platform adopted by 100,000 users worldwide, that is aligned with the Cross-Industry Standard Process for Data Mining (CRISP-DM).

As a trusted partner Crystalloids has implemented KNIME at over a dozen companies over the past few years for use cases such as:

  • Churn analysis and prediction
  • Cross-, Up-, and Deep Sell
  • Sentiment Analysis for Social Media
  • Market Basket Analysis and Recommendation Engines
  • Combining Text and Network Mining
  • Campaign and List Management
  • Lights-out Model Factory

KNIME integrates various components for Machine Learning and Data Mining through its modular data pipelining concept. A graphical user interface allows assembly of nodes for data preprocessing, for modelling and data analysis and visualisation without, or with only minimal programming.

Platform features
The core version already includes hundreds of modules for data integration (file I/O, database nodes supporting all common database management systems through JDBC), data transformation (filter, converter, combiner) as well as the commonly used methods of statistics, data mining, analysis, and text analytics.

KNIME workflows can be used as data sets to create report templates that can be exported to document formats like doc, ppt, xls, pdf, and others. Other capabilities of KNIME are:

  • The core architecture allows processing of large data volumes that are only limited by the available hard disk space (most other open source data analysis tools work in main memory and are therefore limited to the available RAM).
  • Additional plugins allow the integration of methods for text mining, image mining, as well as time series analysis.
  • KNIME integrates with other open source projects, e.g. machine learning algorithms from Weka, the statistics package R project, as well as, LIBSVM, JFreeChart, ImageJ, the Chemistry Development kit, Standford NLP and openNLP.

KNIME is implemented in Java but also allows for wrappers calling other code in addition to providing nodes that allow Java, Python, Pearl and other code fragments to run.

KNIME Analytics Platform now provides easy access to a collection of example workflows using the KNIME Explorer view.  This is the same place where you see your local workflows.

Gartner recognizes KNIME as a Leader in Data Science and Machine Learning Platforms
For the fifth year in a row, KNIME, the open source platform for data-driven innovation, has been placed in the leader category in Gartner’s 2018 Magic Quadrant for Data Science and Machine Learning Platforms. The placement recognises the ability to execute and completeness of vision.

While other platforms are commercialising functionality, KNIME is continuing to cause a stir in the market by adding more functionality to the open source platform. The concept of adding the commercial KNIME Server for collaboration, automation, and deployment, without ever limiting open source usage, is what we are so appreciative of at Crystalloids.

The KNIME difference
Cost-effective: the open source approach enables users and organisations to minimise their data science costs without compromising quality.

Open: the open source nature of its data access, methods and techniques makes it a good choice for collective innovation; simple and relevant ways to enable users to share what they are doing with other members of their organisation. KNIME has a solid commitment to open source and they put everything on GitHub to back that up. This honest, transparent business model works for everyone.

Designed for data scientists of all skill levels: KNIME provides a single, consistent data science framework. It offers highly rated data access and manipulation capabilities, a breadth of algorithms, and a comprehensive machine-learning toolbox suitable for both beginners and expert data scientists. The UI and extensive examples provided with the platform appeal to citizen data scientists.

Automation of model creation and deployment: KNIME Model Process Factory offers automation of model creation and deployment, and of the modelling process, as per the Cross-Industry Standard Process for Data Mining. It also has automated approaches to data quality and feature generation. KNIME can trigger model retraining and supports automated data refresh and synchronisation.

KNIME on Amazon Web Services
The power of Advanced Analytics with KNIME Server running on AWS, eliminating the need for on-premise installation or maintenance, fits perfectly with our vision of bringing your data and applications to the Cloud. Offering all the best features of KNIME Server for scalable, on-demand deployment. KNIME Server brings maximum collaboration with minimum configuration.

Regardless of whether you use KNIME Analytics Platform for Advanced Analytics, Machine Learning, Business Intelligence, or ETL tasks, you can use KNIME Server to extend analytics to your team. KNIME Server offers shared repositories, advanced access management, flexible execution, web enablement, and commercial support.

Crystalloids’ KNIME-related trusted partner consulting services
We offer the full range of services around KNIME, such as:

  • Installation and configuration
  • Architecture design
  • Staff training and coaching
  • Creating new models and rebuilding existing models
  • Creation of process templates

Do you want to get acquainted with the extensive possibilities of KNIME? Follow our 2-day training programme. Tailor-made for your organisation.

For more information please contact us.

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