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CD4ML for building an intelligent enterprise
Taking machine learning from the labs into production, successfully.
The availability of massive amounts of computing power on demand allied to advances in machine learning algorithms — and the existence of massive amounts of digital data with which to train these algorithms — opens up the possibilities for endless improvements in enterprise intelligence.
However, the process for developing, deploying, and continuously improving AI/ML applications is complex. As per a VentureBeat report, 87% of ML projects never make it into production. MLOps extends DevOps into the machine learning space. It 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. CD4ML is a standard at Thoughtworks for ML projects, because it has demonstrated success, and is one that incorporates modern, cloud-friendly software development practices.
Combining Thoughtworks’ data expertise, engineering rigor and architectural innovation with the power of AWS services, we help you place data at the heart of your competitive differentiation.
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 with 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.
Webinar by Thoughtworks and AWS on How to get MLOps right
In this webinar, Randy DeFauw from Amazon Web Services and Eric Nagler from Thoughtworks will talk about how MLOps and CD4ML combine the creative process of data scientists with software engineering methods. In addition, our experts will use customer examples to show you how you can put ML products into practice safely, quickly and sustainably.
Getting Smart: Applying Continuous Delivery to Data Science to Drive Car Sales
In order to tackle the challenges in bringing ML to production, Thoughtworks has developed continuous delivery for machine learning (CD4ML), an approach to realize MLOps. In 2016, Thoughtworks built a price recommendation engine with CD4ML on AWS for AutoScout24, the largest online car marketplace in Europe. Find out more about this project.
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