Many challenges, however, are in the process of being addressed, according to Saif Ahmed, product owner of machine learning at Kinetica. “First, there are so many ways to learn the skills needed to work with AI and machine learning that it’s hardly a specialty at most companies. Google and the other big tech companies may still have access to the most cutting-edge data researchers, but for the rest of us, it’s not an impossible task to find employees with the skills in the right wheelhouse. Secondly, every year, the amount of computing power you get for the same dollar goes up, while the amount you need for a successful ML project goes down. This means funding and housing the necessary computing power is much more feasible. Lastly, data is being recognized across industries as a crucial business asset. There are entire startup ecosystems built around data—data cleaning, data capture, data-cleaning-as-a-service—it’s not an obscure resource anymore.”
PREPARING FOR THE AI AGE
AI-managed data environments may alleviate data staffs from many low-level tasks, but getting there requires having the right skill sets across the enterprise. “There are still skills that any professional within data management and analysis should always be honing,” said Carr. “For example, data engineers and data scientists who have an understanding of the business objectives will be needed.” Even for those that stay in traditional IT, “dealing with the evolution of IT application development and operations IT management tools will require an understanding of AI/ML as it will be embedded in all the product road maps for what they are currently using,” Carr added.
Data managers, engineers, and admins should expand their skill sets to include “a general understanding of AI and ML modeling concepts and use cases—ranging from classic predictive and ML to deep learning frameworks,” said Kazmaier. “Their expertise and support will be needed in the integration and operationalization of AI and machine learning scenarios in applications.” AI and machine learning is also bringing about increased demand for “data scientists who will help steer and support AI and machine learning initiatives in system operations,” he said.
Such advanced skills are necessary, as AI development and deployment is a complex workflow, said Bergh. “If executed manually, it is slow, error-prone, and inflexible. The actual output of the model development process—a set of files, scripts, parameters, source code—is only a small fraction of what it takes to deploy and maintain a model successfully. In addition to ML code, there is data collection, data verification, feature extraction, serving infrastructure, monitoring, and more. The technology and infrastructure supporting the model are actually more substantial and critical than the model itself.”
Being able to analyze data is one thing, “but the ability to present the findings in an easily digestible way will help set you apart from your peers,” said Alex Ough, senior architect-CTO, Sungard Availability Services. “Even if the data analysis is well done, it will not make a difference if you cannot present it to others, especially people without related knowledge. It’s critical to learn how to tell a story based on the findings, and then visually present that story in the most efficient way possible.”
Start small, think big, Ahmed advised. “The ones who will do machine learning well are going to be the ones who stay focused. Most organizations have thousands of use cases where machine learning could be applied. By picking specific issues like the optimal number of shipments or pricing, you can see more significant results. Smart adopters will tackle the low-hanging fruit and work their way up. And some of these smaller, easier projects can come with big dollar signs that make it clear whether or not your model is working. If you can measure your work, you can sustain your project.”