Taking machine learning from the labs into production, successfully.
Artificial intelligence and machine learning (ML) applications are becoming increasingly popular, however the process for developing, deploying, and continuously improving them is complex. As per VentureBeat report, 87% of projects never make it into production. MLOps extends DevOps into the machine learning space. MLOps refers to the culture where people, regardless of their title or background, work together to imagine, develop, deploy, operate, and improve a machine learning system.
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In most organizations, it takes between four months to a year to launch their first ML minimum viable product (MVP), according to the Harvard Business Review. Want to know how to tackle the complexity of building and deploying machine learning in your organization? Read this ebook to find out how.
Featured insights
Whitepaper on MLOps: Continuous delivery for machine learning on AWS
This whitepaper outlines the challenge of productionizing ML, explains some best practices, and presents solutions. ThoughtWorks introduces the idea of MLOps as continuous delivery for machine learning. The rest of the whitepaper details solutions from AWS and other partners.
Staying Nimble, transforming fintech using machine learning
A key enabler of Nimble's brand promise of "Making Finance Faster" is the adoption and implementation of an industrialized ML capability. Find out more about their transformation.
