Breaking Proof of Concept Cycle: Machine Learning from Idea to Production

Machine Learning and AI are beginning to show value across multiple industries for those organisations actively deploying them at scale. However, many are often trapped carrying out Proof of Concept projects, experimenting and developing models with teams struggling to implement solutions and reach production. During this talk from The Data Analysis Bureau, we’ll explore the value of breaking the PoC cycle and how to retain the services in demand and deploy them through a development pipeline to reduce the costs of innovation. We’ll address how you move between R&D and application, get out of the PoC loop, the criteria, tools and timescales should you apply, and how you assess the value to achieve rapid deployment. We’ll share our lessons and a case study from working with academia and industry to move machine learning and deep learning models from R&D into production. Key Takeaways: -Deliver value quickly and affordably – even if it isn't with the most complex algorithm -Understand the difference between application and research, and when to transition between the with a proof of concept -Put the horse before the cart when moving from POC to production -Start delivering with the data infrastructure you already have -Engage your human workforce