A Guide on How to Scale AI:
From Proof of Concept to Production
While the vast majority of companies are aware of the possible benefits of integrating AI applications to their products and internal business processes, few are succeeding in delivering AI at scale and seeing returns on their investment. Those that are, stand to achieve a severe competitive advantage.
Find out how you can bridge the 100m adoption gap with our guide.
A Few Key Lessons
The +$100m Gap
Don’t take our word for it…
The impact of machine learning at scale is only realised by 16% of companies according to Accenture.
Furthermore, Gartner predicts that 50% of IT leaders through 2023 will struggle to move their AI projects past proof of concept and into production.
The Data Analysis Bureau
We specialise in delivering machine learning applications and solutions, and we’ve encountered the challenges many business face when delivering AI projects and worked hand in hand to navigate their adoption.
The inputs to this guide come from the real frustrations of data scientists and engineers trying to drag their organisation into the 21st century. So, if you are a business leader, take note! This is what your data science and engineering team have been trying to tell you, but might be feeling like they’ve not been heard!
Dr Eric Topham, CEO & Data Science Director
5 Key Lessons to take Machine Learning to Production
Machine learning is helping businesses drive industry wide innovation and it is rapidly picking up pace with the increasing adoption and maturity of both technology and the teams that implement them.
But all too often, businesses get stuck in the adoption gap by overcomplicating their move to production.
With this guide, we want to help you break through the Proof of Concept Cycle and deliver AI at scale. Here are our 5 key lessons to help you deliver successful machine learning & AI applications at scale.
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