In recent years Artificial Intelligence and Machine Learning have become the hottest topics, promising significant improvements across a range of industries.
Media substantially covered the breakthroughs achieved using novel AI/ML algorithms in many areas; and in particular, the neural networks and deep learning approaches behind the likes of AlphaGo. Following the trend, many companies made significant steps in areas of AI/ML. The typical approach is to take one or more business cases and seek to apply AI/ML technologies of different readiness levels to solve them.
However, there are no free lunches and no silver bullets here – finding the algorithm or technology that is best suited to a particular use case is mostly down to investigative trial and error, guided by experience.
To manage risk, companies put effort and resource into Proof-of-Concept (PoC) projects aimed at establishing whether an approach really can solve the given business objective and where the focus is on the quality of the algorithmic performance. Many organizations find that converting working PoCs into industrialized solutions is much more complicated than expected, with seemingly inconsequential decisions made during the PoC phase leading to significant problems further downstream.
A report issued by Pactera says that 85% of all AI projects fail. It also highlights that the transition from PoC to production usage the critical step at which most AI projects fail. What are the challenges and risks that make AI systems so hard to operationalize?
In this whitepaper, we will cover the key challenges to AI/ML industrialization and how the use of AI DevOps can streamline AI/ML development and deliver successful projects at scale.
Download our white paper to learn more:
Operationalization of artificial intelligence at scale