TwinTech Labs Pushes to Democratize Access to Machine Learning With Its Open Source ML Platform Launch
The future of applied machine learning is codeless.
TwinTech Labs today announced the release of ioModel, an advanced ML platform designed to allow companies to seamlessly make the transition to being a model-driven organization by leveraging their existing analytics teams, business analysts, and engineers. The ioModel ML Platform is developed entirely using open source technology and is itself available under the GPL License on GitHub.
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“The ioModel ML Platform will do for artificial intelligence what the spreadsheet did for general business computing,” said Matt Hogan, CEO, TwinTech Labs. “Rather than building another comprehensive analytics platform, we focused on building a product that integrates well with the existing analytics landscape while streamlining model creation, deployment, and integration workflows.”
The Worlds of Analytics and AI are About to Converge and Change Forever
Many companies today still have a traditional BI team focused on developing insights and providing decision support while maintaining a separate data science organization responsible for designing customer facing machine learning models and product features. Often, these two teams are working on similar problems, require similar access to data, and have significantly overlapping statistical and analytics skill sets.
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ioModel challenges this approach by providing an innovative environment where anyone with access to data in an organization can easily understand the relationships between pieces of data, create and evaluate the efficacy of predictive models, and seamlessly integrate their work with the broader engineering organization. Our new paradigm significantly reduces the costs associated with research and development of model-driven features and supports the transition to becoming a data-driven organization.
From Concept to Model to Market. Faster.
There is currently a proliferation of code-based libraries and frameworks designed to simplify most data science problems and processes. However, each of these frameworks has a significant learning curve, requires specially trained staff, and still results in hundreds to thousands of lines of code that need to be maintained and could contain faults leading to inaccurate models and lost revenue. The ioModel ML Platform sidesteps this problem by automating coding tasks needed for model development, evaluation, and deployment. Because there is no code to develop or maintain, organizations can create new models in 15 minutes to an hour.
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