KNIME Solves Key Enterprise Challenges to Use Data Science in Production
- Production: Integrated Deployment closes the gap from model creation to production
- Collaboration: Guided Analytics balances custom automation and human interaction
- Execution: KNIME Executors now enable more flexible, hybrid and elastic deployments
- Compliance: Metadata Mapping delivers full documentation for simplified auditing
ZURICH & AUSTIN, Texas–(BUSINESS WIRE)–#AutoML—KNIME today announced four major updates — Integrated Deployment, Guided Analytics, Flexible Executors, and Metadata Mapping — that overcome significant enterprise challenges. These offerings are designed to make data productive for the organization and close the gap between data science and business.
“For predictive models, there is usually a hard stop after creation, and manual steps are needed for deployment, resulting in lots of friction in the enterprise,” said Michael Berthold, CEO and co-founder of KNIME. “With our latest developments, we enable enterprises to put their models into production in an integrated, scalable and compliant way.”
KNIME is uniquely positioned to bring each of these updates to market based on the combination of its open source KNIME Analytics Platform and commercial KNIME Server. With this unique offering, enterprises can make data science available throughout the organization, controlling every step of the process and paying only for what is actually needed.
Production: Integrated Deployment Closes the Gap From Model Creation to Production
- Keywords: ModelOps, ML Ops, Model Deployment, Last Mile of Analytics
KNIME’s Integrated Deployment generates real value for the enterprise through seamless transition from model preparation to production and optimization. Along with opening Integrated Deployment to its entire user base, KNIME publishes new customizable solutions for guided machine learning and continuous deployment.
Integrated Deployment, which is unique to the industry, writes out all relevant parts of a workflow to be used in production and manages it from the same platform. It not only includes the model chosen for production but also any other relevant part of the creation workflow, such as customized data preprocessing. This technology is easy to use, error-proof and resource-efficient for productionizing data science, and it also enables continuous optimization by providing an infrastructure to monitor and automatically update workflows in production.
Collaboration: Guided Analytics Balances Custom Automation and Human Interaction
- Keywords: AutoML, Data Citizens, Customized Solutions, Packaged Business Capabilities
Guided Analytics applications can be customized based on reusable components available on the KNIME Hub and made available to the end user via KNIME WebPortal. The WebPortal has just been released with a completely new UX/UI and gives non-experts intuitive access to data science by empowering data science experts and business consumers working together in an integrated way.
Execution: KNIME Executors Now Enable More Flexible, Hybrid and Elastic Deployments
- Keywords: Auto Scaling, Autoscale, Elastic Scaling
KNIME’s flexible execution options leverage enterprise infrastructure choices while covering periods of high demand dynamically. This enables IT to meet computing capacity requirements while controlling cost, for example, by mixing and matching on-prem data centers with cloud resources. Additionally, arbitrary workflows can now be executed on an elastic scaling environment with only one click.
Executor Groups and Reservation are new features in KNIME Server, which is now available on the AWS marketplace as bring-your-own-license, while AWS Auto Scaling can also be used on a pay-as-you-go basis with KNIME.
Compliance: Metadata Mapping Delivers Full Documentation for Simplified Auditing
- Keywords: Data Lineage, Governance, Compliance, Auditability
KNIME’s Metadata Mapping with Workflow Summary makes compliance and governance remarkably easy. It can be used to extract and export every detail of the user’s KNIME workflows — from execution, execution environment, individual node settings, and data sources to high-level, interactive summaries.
This complete documentation can be formatted via simple workflows and doesn’t require additional tools or products. Beyond governance, this capability is used to actively monitor workflow quality and keep all elements up to date, surfacing segments where remote programming and manual transformations have been enabled as well as which changes to data will impact applications and processes further down the line.
Release Webinar: What’s New in KNIME Analytics Platform 4.2 and KNIME Server 4.11.0
For more details, a free KNIME webinar reviewing the updates to KNIME Analytics Platform and KNIME Server will be offered on July 21; registration can be found at www.knime.com/about/events/webinar-whats-new-july-21-2020-us.
KNIME publishes two major releases a year, in July and December.
- KNIME Analytics Platform version 4.2 is an open source platform available for download at www.knime.com/knime-analytics-platform.
- KNIME Server version 4.11.0 is a commercial platform available through KNIME and its partners at www.knime.com/knime-server.
KNIME provides software for fast and intuitive access to advanced data science. At the core is the open source KNIME Analytics Platform, a visual workbench providing a wide range of state-of-the-art analytics tools and techniques to handle any use case — from basics to highly advanced. It is complemented by the commercial KNIME Server which makes data science productive in the enterprise, while staying in the same software environment for deployment, collaboration, management and optimization. Headquartered in Zurich, KNIME has offices in Austin TX, Konstanz and Berlin. Learn more at www.knime.com.
KNIME, KNIME Analytics Platform, KNIME Server, and KNIME WebPortal are trademarks of KNIME. All other brand names and product names are trademarks or registered trademarks of their respective companies.
Tags: KNIME, open source, data science, AWS, autoscale, auto scaling, data analytics, machine learning, deep learning, artificial intelligence, AI, ML, KNIME Analytics Platform, KNIME Server, KNIME WebPortal
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