AI engineering allows organizations to gain value from AI projects while drawing attention to ethical concerns. Learn more about AI Engineering solutions!
If you work closely with artificial intelligence (AI), you likely have heard the term “AI engineering.” After all, AI engineering appeared as the eighth item on Gartner’s Top Strategic Technology Trends for 2021. And with real-time analytics and mechanical reasoning that imitates the human brain increasingly becoming important to a number of industries, it only follows that enterprises need to fully comprehend this concept and the advantages it offers.
As advances in computing power, capacity, speed, data diversity, and deep neural networks continue to climb, more organizations are realizing the need for a strategic approach to this Gartner tech trend. So, how exactly does AI engineering expand on this technology while playing an increasingly pivotal role in business? Read on for our perspective!
Falling under the theme of resilient delivery, AI engineering tackles the challenges AI projects often face, including maintainability, scalability, and governance. It only follows, then, that AI engineering is the use of algorithms, neural networks, computer programming, and other technologies that help develop AI applications and techniques. This year, per Gartner, “A robust AI engineering strategy will facilitate the performance, scalability, interpretability and reliability of AI models while delivering the full value of AI investments.”
With AI engineering, AI is made a part of the mainstream DevOps process instead of a set of specialized and isolated projects. By bringing together various disciplines, this approach tames the AI hype while offering a more direct path to value when operationalizing the combination of multiple AI techniques.
Therefore, an artificial intelligence engineer must have the capabilities to efficiently extract data from many sources, design algorithms, build and test machine learning models, and then deploy those models to create AI-powered applications that can perform complex tasks.
Further, this year, responsible AI – the operationalization of AI accountability – is coming into the spotlight. Because of the governance aspect of AI engineering, more organizations will be launching projects that deal with trust, transparency, ethics, fairness, interpretability, and compliance issues.
Thanks to artificial intelligence engineering, organizations are able to create hybrid operating environments that bring together data scientists, data engineering, and software development. AI engineers can deduce whether or not a project will benefit most from strong or weak AI, and pinpoint which projects have the potential to address a business’ relevant pain points (including customer complaints, supply chains, or other external factors).
AI engineers work closely with other IT and AI specialists to coordinate process automation and data management within an organization. An example of this kind of team work can be found in manufacturing, where AI developers work closely with electrical engineers and software developers to create AI robots. And at a strategic level, business intelligence developers create, model, and evaluate complex unstructured data to identify market trends and industry patterns.
According to research conducted by Gartner, AI will create a business value of $3.9 trillion by 2022. As AI continues to be forecast as the most disruptive technology category for the next decade, careers that demand skills in AI engineering are on the rise.
In December 2019, social media platform LinkedIn ranked the role of an artificial intelligence specialist at number one in its annual list of Emerging Jobs. According to IT Chronicles, an AI engineer should have abilities in the following categories:
The first place to start? An AI engineer should have the ability to work with algorithms, machine learning, neural networks, and other AI-related technologies. At the same time, a competent artificial intelligence engineer is good at programming and has a thorough understanding of the software development lifecycle, coding techniques, and best practices. Those with left-leaning brains will have the necessary mathematical skills in linear algebra, statistics, and probability to understand and enact different AI models.
Further, AI engineers should have some familiarity with memory management, classes, and linking in order to take on projects that require hardware integration to increase speed. When it comes to natural language processing, AI engineers draw from computer science, information engineering, linguistics, and AI to program systems that process and analyze large data sets. Finally, the ability to rapidly prototype and A/B test gives AI engineers a leg up when quickly iterating on various ideas and deciding which one is best suited for the task at hand.
Alongside technical skills, an AI engineer can govern the performance of a machine learning model and decide when it is ready for deployment (while monitoring its accuracy over time). By quickly adding machine learning capabilities to existing business resources like enterprise resource planning or customer relationship management systems, AI engineers can add value to an organization’s business processes. Because AI engineering has to provide applications specific and relevant to the business, AI engineers should understand how the business runs, its customers, and market conditions and operating environment. A successful AI engineer can translate their technical ideas into practical and efficient business models.
Don’t forget soft skills like communication and collaboration! AI engineers will often be tasked with helping the company develop innovative and appropriate solutions to its problems. Therefore, they need knowledge about their industry, a creative mind, and the ability to think analytically when it comes to problem-solving. Further, AI engineers must be able to simply and directly communicate their ideas effectively to all relevant stakeholders both within and outside of the enterprise.
Artificial intelligence is a big part of what makes Stefanini innovative. Our AI platform is called “SAI,” which stands for “Stefanini Artificial Intelligence.” SAI is the technological ecosystem behind our complete AI solution. It is supported by complex algorithms and contains services like virtual assistants, computer vision, and data analytics.
Sophie is our omni-channel virtual assistant. With the ability to quickly process large amounts of data, Sophie understands natural language conversations. She can assist users with requests, coordinate communications, and support automation between people and systems.
Are you ready to hear all of the ways Stefanini can support robust AI solutions for your organization? Give us a call today!
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