Maintain Your Product’s Competitive Edge
Firefly.ai for Enterprise Software
The global AI market is expected to be worth almost $60 billion by 2025, and it’s predicted that AI will contribute around $15.7 trillion to the global GDP by 2030. It’s not surprising that product leaders in enterprise software companies are eager to leverage AI in order to drive growth, develop new products, and maintain their competitive edge.
However, many small and medium-sized businesses lack the data science resources and personnel to lead the introduction of AI. It can take a long time to reach delivery of MVP, and productization is complex.
Firefly.ai’s automated machine learning platform (AutoML) enables enterprises to speed up time to MVP and smoothly productize their models even without a robust data science team. Here are the two most important use cases for automated machine learning in the enterprise sector.
Fast MVP delivery
Machine learning and AI can significantly improve enterprise software products, but spotting the ideal use case could take months. The Firefly.ai AutoML platform enables product leaders to test models and identify the best use case within just a few days, without having to write a single line of code or possess advanced knowledge of data science.
AutoML helps enterprise companies to move to productization. With Firefly.ai, product leaders can deploy, productize, and maintain predictive models with one click and retrain and refit models on the fly, to keep on innovating without a pause.
Firefly.ai can be deployed in the cloud, using the Firefly.ai API, or as an on-prem solution through Docker.
Firefly.ai brings faster innovation to the enterprise software industry
Firefly.ai provides product leaders in enterprise software companies with the tools to identify new use cases and move to MVP in the shortest possible time frame. AutoML and Firefly.ai can speed up innovation and reduce time to market for new enterprise software products, while one-click productization helps enterprise software companies to deploy and maintain predictive models, without the need for an experienced data science department.