Accelerate and Simplify ML for KNIME Users with H2O.ai AutoML
The expanded KNIME integration with H2O.ai brings together all-encompassing, intuitive, automated machine learning from H2O.ai with the self-service data preparation, blending, and guided analytics from KNIME. This joint solution provides further automation to end-to-end data science life cycle from data prep, AutoML, MLOps to continuous optimization. As part of the collaboration, customers of H2O.ai and KNIME can now:
- Enable fast, reliable, and flexible access to data, insights, and predictions in enterprise data
- Build a single integrated visual data science workflow from data preparation to AutoML and to model production
- Simplify and speed up machine learning with the power of H2O Driverless AI automatic machine learning to business analysts in the KNIME Analytics Platform and KNIME Server
- Productionize H2O Driverless AI models(MOJO) using KNIME drag and drop canvas for model management and monitoring
Accelerate ROI on AI Initiatives
* Expand Automation
Automate end-to-end Data Science Workflow
* Simplify Data Preparation
Drag and Drop Data Sources and Transformation Processors
* Ease of Use
Quickly and intuitively leverage H2O Driverless AI with KNIME Workflow
* Accelerate Model Training
Build better models in less time with Driverless AI AutoML
* Seamless Deployment
Deploy H2O and Driverless AI Models (MOJOs) into KNIME Workflows
KNIME Integration Overview
KNIME users have been taking advantage of H2O-3 in KNIME since 2017 and can now seamlessly use H2O Driverless AI with a new extension available from the KNIME Hub. This new integration empowers data scientists or data analysts to work on machine learning projects faster and more efficiently using automation and state-of-the-art computing power to accomplish tasks that can take humans months in just minutes or hours.
- Develop an integrated data science workflow in KNIME Analytics Platform, from data discovery, data preparation to production-ready predictive models
- Deliver the power of automatic machine learning to business analysts, enabling more citizen data scientists with H2O Driverless AI
- Reduce model deployment times, leveraging H2O Driverless AI and KNIME Server for reliably managing production deployment process
KNIME users can leverage Driverless AI in a workflow to provide automatic feature engineering, model validation, model tuning, model selection, machine learning interpretability, time-series, NLP, computer vision, and automatic pipeline generation for model scoring. H2O Driverless AI provides companies with a data science platform that addresses the needs of various use cases for every enterprise in every industry.Read Blog
Simplify and Automate Data Preparation KNIME
KNIME provides a rich set of data source connectors and data preparation nodes with a no-code drag and drop canvas to simplify data access and preparation. This empowers data analysts, data engineers, and data scientists to quickly build data preparations flows to prepare, wrangle, clean, join, and filter the data and get it ready for machine learning. Once the data is prepared, it can connect to H2O-3 or Driverless AI to build the machine learning models within the same drag and drop canvas.
Driverless AI AutoML in KNIME
H2O-3, and now H2O Driverless AI, enhance the capabilities of the KNIME Analytics Platform and Server with automatic feature engineering and machine learning. This integration enables data scientists, analysts, and business users to develop trusted machine learning models from their existing KNIME environments and run through the entire data science workflow seamlessly.
Make Predictions in KNIME Flows
KNIME can build Machine Learning production workflows to consume the models that were trained with Driverless AI. H2O.ai provides production-ready low latency models and pipelines in the MOJO deployment artifact. MOJO (stands for Model Object, Optimized) is a standalone, low-latency model object designed to be easily embeddable in a wide variety of production environments. KNIME provides H2O MOJO and now H2O Driverless AI MOJO Predictor nodes to score data within a KNIME Workflow via drag and drop interface.
Extended No Code Automation
Preparing data for Al, selecting the right model, pushing it into production, and continuously optimizing it is a process that typically requires many stakeholders and several tools. Parts of this end-to-end process can be automated, but flexibility is paramount to select the techniques that address a company’s machine learning use case in the best way. This H2O.ai and KNIME integration provides a solution that covers all of these challenges and increases data scientists’ productivity, reduces overall IT spend, and delivers more accurate predictions.
Data Analysts, Data Scientists (from novice to experts), and Data Engineers can now combine KNIME and H2O.ai workflows for data access, preprocessing, ML, deployment, and optimization automate any or all of this process without changing environments. This can be done visually and without coding, but with the flexibility to code if needed. Users can either visually build a workflow and leverage AutoML to train model, deploy a REST service, or end-user application or mix with code to achieve the optimal method for your specific problem.