Is It Time to Take AI Engineering Seriously?

AI Engineering

One of the innovations that completely transformed the world was electricity. The next innovation of comparable magnitude that influences the way we live and work is that of AI. AI perhaps is the hottest, most disruptive technology in the tech-driven market today. 

The AI market size was valued at USD 62.35 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028. 

AI applications across industries are also increasing and this technology is seeing favorable adoption across industry verticals such as healthcare, retail, finance, manufacturing, and automotive. From self-driving cars to personal protective equipment to drug discovery; AI is on its path towards becoming a mainstream technology that drives productivity and efficiency while reducing human effort. 

The Rise of the AI Engineering 

As the scope of AI expands across industries like healthcare and finance, there arises the need for AI engineering. AI engineering focuses on developing smart tools, machines, and systems to enable AI applications in the real-world context. It explores the boundaries and limits of AI systems in practice today and questions elements like the limits of AI systems in current practice, how to ensure ethical standards as AI systems are deployed, and the like.

Why AI Engineering?

Today, computing power has increased and massive data sets are available for creating new AI algorithms and models encompassing thousands of variables and capable of making impactful decisions. However, too often, these capabilities only work in controlled environments and can be extremely hard to replicate, verify, and validate in real-world situations. 

AI engineering aims to guide the development and deployment of AI applications and deliver a framework of tools that guarantees that the AI systems will work in environments that have high levels of complexity, dynamism, or ambiguity. 

AI engineering helps us navigate this dynamic and disruptive business landscape. It assists professionals to anticipate needs in the constantly evolving and shifting operational environments, translate human demands into ethical, trustworthy, and understandable AI applications, and develop systems across the enterprise-to-edge spectrum. 

AI engineering needs a more evolved development process that combines principles of software engineering, systems engineering, computer science along with human-centered design to create AI systems. Certain concerns in traditional software systems are exaggerated in AI systems as well, especially in systems that employ Machine Learning components.

AI engineering needs a more advanced development model as compared to other software development models because:

  • Data discovery, management, and versioning in Machine Learning/ AI applications are of much higher complexity as compared to other kinds of software engineering. 
  • Organizations need specialized skills for model customization and model reuse – these skills are not easily found in software development teams. 
  • AI components are harder to manage as the models can get entangled in complex manners and can experience non-monotonic error behaviors. 
  • Accumulating high technical debt becomes a possibility since it is important to avoid or refactor specific risk factors and design patterns where possible. Elements like boundary erosion, data dependencies, hidden feedback loops, entanglement, undeclared consumers, external world changes, and a range of system-level anti-patterns are critical areas to keep an eye out for.
  • AI systems need to be developed for inherent uncertainty; accounting for uncertainty in components, data, models, and outputs thus become essential. The rate of change is also not consistent in AI systems. Data and models could frequently change and might or might not imply changes across the system, and, as such, validating the need for AI engineering
  • AI systems need to be engineered to become more secure since changing data and underlying models could account for ambiguity and provide increased attack surfaces.

AI engineering practices aim to alleviate these and other such challenges and give organizations the capability to create viable, trusted, and extensible AI systems.

AI engineering needs subject matter experts, data scientists, and data architects in software engineering teams to make up a strong core for AI systems. Areas such as data ingestion, cleansing, monitoring, protection, and validation need clear and defined processes and immaculate attention since the system output is directly related to data used to train the system. Applying highly integrated monitoring and mitigation strategies becomes essential due to the complexity of models. Incorporating user experience and interaction for model validation and their evolution is also a crucial area of attention.

In Conclusion

It certainly is time to look at AI engineering as the hesitance towards using AI reduces and is replaced with enthusiasm for leveraging this technology to drive greater intelligence into software systems and products. 

However, AI engineering has to ensure that the problem at hand must be solved with AI only as other simpler options do not suffice. While we can get excited about this fantastic technology, it pays to remember that AI, after all, is not a panacea. It can be a far more complex and less effective solution when the problem does not lend itself specifically to AI.

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