Spaces of knime
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AI Extension Example Workflows
This space offers a curated collection of KNIME workflows, demonstrating practical applications in large language models, chat models, vector stores, and agents. It serves as a resource for understanding how to effectively utilise various AI capabilities within the KNIME environment for various tasks.
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Beginners Space
The "KNIME Cheat Sheet: Building a KNIME Workflow for Beginners" is a great starting point for Beginners to start learning about KNIME. With this dedicated space, we showcase the usage of nodes mentioned in the Cheat Sheet to build simple workflows. The workflows are segregated into 5 categories: Read, Explore, Transform, Analyze and Deploy which is in line with the node categories defined in the Cheat Sheet. How to use this Space? Go to the "01_Read" folder and drag workflows to your KNIME Analytics Platform to learn reading data from the various file systems into KNIME, likewise go to the other folders and learn how to explore, transform, analyze data and then deploy using KNIME The Cheat Sheet "Building a KNIME Workflow for Beginners" is available for download at: knime.com/cheat-sheets If you want to give your acquired knowledge a test run, take a look at the Just KNIME It challenges at: knime.com/just-knime-it
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Codeless Time Series Analysis with KNIME
This space contains the example workflows from the "Codeless Time Series Analysis with KNIME" book (published 2022).
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Continuous Deployment for Data Science
This space contains the installation workflow for CDDS and example projects that can be used within CDDS. Documentation and guide for Continuous Deployment for Data Science (CDDS): http://docs.knime.com/latest/business_hub_cdds_guide/index.html
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Digital Healthcare
KNIME solutions for classic problems on Digital Health
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Education
This space offers all exercise workflows offered by the KNIME Evangelism team. Visit knime.com/courses and knime.com/educators to learn more of the free teaching materials that KNIME offers to learners and teachers!
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Educators Alliance
This space contains materials (example workflows, assignments, etc.) for teaching with KNIME Analytics Platform.
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Examples
Explore this space for workflows and verified components provided by us at KNIME to use as blueprints and building blocks for creating workflows to solve your own data science use cases.
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Financial Services & Banking
Find solutions for retail, commercial, and investment banking. For back office tasks see the Finance, Accounting, and Audit space.
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Just KNIME It!
Solutions for the "Just KNIME It!" challenges, uploaded weekly to this Hub space.
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KNIME Business Hub Admin Workflows
The workflows of this space aim to support KNIME Business Hub (KBH) Administrators, or heavy KBH users, to clean up, monitor and better administrate their Business Hub installation. The functionalities provided are a time saver for monitoring or administrating KBH avoiding manual work, and centralising different information among the applications. To execute these workflows, they should be run as data applications or scheduled deployments in the KNIME Business Hub.
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KNIME Business Hub Deployment Examples
The KNIME Business Hub has significantly expanded the range of options for deploying workflows. We have prepared a variety of deployment examples that serve both as demonstrations and real-world use cases, offering a comprehensive showcase of the platform’s latest features. To configure these examples within the KNIME Business Hub, the first step is to initiate an installation workflow.
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KNIME Edge
This space contains workflows for initializing and utilizing a KNIME Edge cluster. Please contact our customer care team for information on getting started with KNIME Edge. KNIME Edge is a distributed, container-based platform that moves the consumption of models directly to where data is generated. Built on top of Kubernetes, KNIME Edge offers the ability to deploy workflows as highly available and scalable endpoints. This allows for high throughput and low latency while also decentralizing execution by deploying into data centers, manufacturing facilities, multiple cloud providers, and more. One or more KNIME Edge clusters can be remotely managed by leveraging KNIME Server. Using KNIME Server’s WebPortal, a user can select and deploy workflows to any connected KNIME Edge clusters. Once a workflow is deployed, KNIME Edge creates locally consumable endpoints while managing execution, scaling, uptime, resiliency, and more to ensure model applications can scale seamlessly with demand.
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KNIME for Finance
This space is for blueprints in the field of finance, accounting, and auditing. Use these blueprints not only as solution starters but to get an idea about the capabilities KNIME provides for these fields. For example: Access and combine data from various sources, clean them, and show in reports; automate processes and escape repetitive copy / pasting; build a transparent, no-code workflow instead of complex macros
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KNIME for Spreadsheet Users
Find examples of the most common data manipulation tasks. They are recommended for beginners, particularly spreadsheet users, who are new to visual workflows.
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KNIME Press
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Life Sciences
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Machine Learning and Marketing
This live repository contains example workflows of common data science problems in Marketing Analytics. The original task was explained in: F. Villarroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036. Please cite this article, if you use any of the workflows in the repository.
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Parameter Optimization Space
In this space we show both simple and complex workflows to learn how to fine-tune parameters for a generic classification model. Browse through those workflows to learn how to combine those nodes and components with any learner and predictor node which KNIME offers for ML classification.
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Process Mining
This space offers a repository of example workflows for process mining. These examples vary by industry/domain, use case and nodes/components adopted.
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Python Script Space
This Python Script Space aims to provide users with workflow examples on various use cases involving the Python Script node. Read more at: knime.com/blog/python-script-node-bundled-packages For detailed information check out the KNIME Docs at: docs.knime.com/latest/python_installation_guide The workflows are segregated into five folders as below: In 01_Getting_Started, the workflows will help you understand how to add/remove various input and output ports and access the input table or objects. In 02_Using_Bundled_Python_Packages, the workflows demonstrate the usage of bundled packages like NumPy, pandas, scikit-learn etc. In 03_Using_Custom_Python_Packages, the workflows demonstrate the usage of other python packages that are unavailable in the bundled environment and custom-defined python classes. In 04_Sharing_Python_Scripts_in_Components, the workflows showcase the creation of Scripted Components with Python. In 05_Jupyter_Notebook, the workflow demonstrates the usage of Jupyter Notebook inside the KNIME workflow
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Server to Hub Migration
A workflow and a user guide to help you with the migration of workflows, files, snapshots, and schedules from your KNIME Server to your KNIME Hub instance.
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Supply Chain
This live repository contains example workflows of common data science problems in Supply Chain. Additionally this space contains examples of the verified component "Progress Tracker View".
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Topic Modeling
In this space we show examples on how to use KNIME nodes and verified components for topic modeling.
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Workflow Snippets
Find simple workflows for new users to KNIME demonstrating how to solve specific tasks or use specific nodes plus our favorite tips and tricks to help you along your learning path.
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XAI Space
This space offers a collection of examples workflows for XAI. These workflows showcase how to use both: - Nodes from the KNIME Machine Learning Interpretability Extension: --> kni.me/e/IaoFfMScprMvBCY- - Model Interpretability Verified Components: --> hub.knime.com/knime/spaces/Examples/latest/~WMtQn1U91a-xzZY3/ These XAI examples can help you understand and interpret your machine learning model trained in KNIME. The workflows are divided into 2 main categories based on the ML Task : Classification and Regression. There are then also two sub-categories based on the type of training: AutoML and Custom Models. More infos on KNIME Blog: knime.com/blog/download-explainable-xai-solutions-hub KNIME Press Booklet download: knime.com/knimepress/explainable-ai
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