Enterprises have been using analytical machine learning techniques for years to solve business problems related to making predictions on raw data. Today, perception-based techniques driven by deep learning and neural networks are gaining traction around understanding vision and language, both of which have applications within enterprise settings.
When people speak of enterprise artificial intelligence (AI), it is quite common to treat it as a generic term or one large entity without being specific about what technology is being used. This can lead to misunderstandings around what AI can and cannot do, the software and hardware that is required, or even the talent needed to develop the AI solution.
According to Tractica’s definition of AI, statistical ML techniques like random forests, support vector machines, naive Bayes and linear regression, among others, fall under the umbrella term of AI. However, these techniques should be treated separately from the deep learning branch of AI. Deep learning seems to get the lion’s share of artificial intelligence attention due to its association with large internet companies like Google, Facebook and Amazon, as well as the cloud-based AI frameworks that have been in the news. So to understand the issues that are specific to machine learning, we decided to question machine learning developers about the challenges they are facing, the software and hardware tools they use and the application markets where they see most activity.
Our resulting survey – conducted recently by Tractica in collaboration with ITPro Today – has uncovered specific trends related to data science and machine learning development within the enterprise. The survey garnered responses from 50 machine learning developers.
Bottlenecks to Enterprise ML Development
Among bottlenecks faced by ML developers, data preparation ranks at the top, followed by external code integration and enterprise back-end integration. This is consistent with what we have been hearing from enterprises of all sizes. Cleaning data, labeling data and checking for bias in data are all part of the hard plumbing that is required today to make sure ML-driven AI processes are working. At the same time, the ability to bring code from external environments continues to be challenging, though vendors claim that this is not the case.
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