KNIME, one of the leading open-source data science and AI companies, has raised additional funding from its longstanding investor Invus. Since the last announcement, Invus invested another $30M, bringing total funding to $50M. This will allow KNIME, as the only open-source, low-code data science company on the market, to double down on development of its enterprise-grade AI governance and ModelOps capabilities, while continuing to stay ahead of the rapid innovation in the field.
Data Science and AI Company KNIME Secures Investment to Accelerate Adoption of Enterprise-Grade AI Governance and ModelOps
August 6, 2024 by
Machine Learning Model Management: Ensemble Modeling
August 23, 2022 by
In this contributed article, editorial consultant Jelani Harper highlights how the machine learning approach called ensemble modeling enables organizations to utilize an assortment of models and combine them, and their predictive accuracies, to get the best result.
XOps: The Rise of Smarter Tech Operations
March 15, 2022 by
In this contributed article, Arvind Prabhakar, CTO of StreamSets, discusses how XOps has become a growing data analytics trend in 2021 for good reason. Evolved from the DevOps movement to better support and enable AI and ML automation workflows, XOps enables organizations to operationalize data and analytics to drive greater business value and establish a solid yet flexible foundation for future technology development.
The ModelOps Movement: Streamlining Model Governance, Workflow Analytics, and Explainability
April 9, 2021 by
In this contributed article, editorial consultant Jelani Harper discusses how the ModelOps movement either directly or indirectly addresses each of the following three potential barriers to cognitive computing success: model governance, explainability, and workflow analytics.
2021 Trends in Artificial Intelligence and Machine Learning: The ModelOps Movement
November 3, 2020 by
In this contributed article, editorial consultant Jelani Harper highlights the ModelOps movement and how it is about more than simply operationalizing and governing AI models. It’s about doing so quickly, at scale, with full accountability, and in a manner that resolves the most mission critical business problems—if not those for society, as well.
