There would be no wrong in claiming that artificial intelligence has seemingly endless potential to simplify and accelerate time-consuming and complex tasks. From image processing to speech recognition, from business process management to diagnosis of disease, artificial intelligence is playing a pivotal role in revamping the dimensions of this growth-oriented world. 

Numbers never lie. The AI market is experiencing tremendous growth all across the globe. Statista reports a global market size of $327.5 billion back in 2021. Moreover, the AI market share in the United States of America is expected to value at $190.61 billion by the year 2025. Also, Gartner estimates that AI software will reach $62 billion only in 2022, an increase of 21.3% from 2021. On top of that, Semrush forecasts that the compound annual growth rate (CAGR) of artificial intelligence between 2020 and 2027 is 33.2%. 

The availability of off-the-shelf solutions and ML libraries gives the impression that the implementation of AI-powered software solutions is easy. However, developing trusted and viable AI systems that are deployed to the field can be extended and evolved for years and requires ongoing resource commitment and significant planning. 

The following practices are the AI engineering recommendations for decision-makers. So without any further adieu, let’s jump right into them. 

  • Problem Identification 

Ensure that you are facing a problem that artificial intelligence can and should solve. Begin with a well-defined problem. Developing an understanding of what is your goal and what is the outcome you need while making sure that you have data available to infer those outcomes. After the identification of the problem, you can come up with the best solution among all the available options. AI is not a nostrum. It is often a complex and less efficient solution for challenges where other solutions may already exist. 

  • Seek The Assistance of Experts 

Include highly integrated subject matter data scientists, experts, and data architects in your software engineering team. Effective AI engineering teams should include experts in the problem’s domain a.k.a subject matter experts, model selection and refinement, data engineering, hardware infrastructure, and software engineering along with other typical software engineering expertise. Include team members who can deal with the sparsity of well-designed tools and the system’s high demands in terms of scalability, versioning, bandwidth, performance, and resource management.   

  • Take Data Seriously 

Take your data seriously to deter it from consuming your project. Monitoring, cleansing, protection, data ingestion, and validation are necessary for engineering successful AI systems. They require tremendous amounts of resources, time, as well as attention. Make sure that your processes account for the following: 

  • Possible bias
  • Changes in the environment 
  • Possibility for adversarial exploitation during the system lifetime. 
  • Choose Algorithms as Per Requirements 

Do not choose an algorithm on the basis of popularity. Choose an algorithm on the basis of your business’s model and requirements. Algorithms differ in several significant dimensions, such as what kind of problems they can solve, how detailed the information in the output is, and how robust the algorithm is to adversaries. Businesses should pick an algorithm that is appropriate for their problem. They should go for such approaches that satisfy the engineering and business requirements. 

  • Enhance Security Protocols of AI Systems 

Businesses should take sophisticated measures to secure AI systems with the implementation of integrated monitoring and mitigation strategies. The attack level of an AI system is expanding because of the challenges of understanding the functionality of complex models and data dependency. Such additional attack surface dimensions compound the vulnerability of the traditional software and hardware attack surface. 

  • Define Checkpoints For Recovery 

Businesses should properly define checkpoints to account for the potential requirements of traceability, recovery, and decision justification. AI systems are extremely sensitive to the dependencies among training data, input data, and models. Changes to the characteristics and versions of anyone can directly, or sometimes subtly, affect others. Businesses should carefully consider when and how to correlate input data with the model used to evaluate it in systems where models change continually or frequently. 

  • Incorporate User Experience 

Organizations should incorporate interaction and user experience to evolve and validate models as well as architecture constantly. They should use an automated approach as much as possible to capture human feedback on system output and improve the model. In addition, they should monitor user experiences to detect issues early, such as degraded performance in the form of system latency and reduced accuracy. 

Others

Here are some more recommendations for the experts that you should definitely implement in your organization: 

  • Design for the understanding of the intrinsic ambiguity in the output. 
  • Implement loose-fitting solutions that can be extended or modified to adapt to ruthless and unavoidable data as well as model changes and algorithm innovations. 
  • Commit sufficient expertise and time for constant and enduring change over the life of the system. 
  • Treat ethics as both policy concern and software design consideration. 

Final Verdict 

Along with the recommendations discussed in this article, keep in mind that AI systems are software-intensive systems. Hence, teams should follow modern systems and software engineering practices to keep up their competitive edge in this ever-evolving world. In addition, teams should strive to deliver the functionality of time and with quality design for architecturally significant requirements and plan for sustaining the system for its entire lifetime. 

Needless to say, change is the only constant. Design, deployment, and sustainability of AI systems require engineering practices for the management of inherent uncertainty along with the constant and increased rhythm of change. AI-powered systems are irrevocably evolving. Changes brought by such systems reach across technologies, problems, processes, engineering, and cultural boundaries.  

We, at ArhamSoft (Pvt) Ltd, utilize innovative algorithms, practices, and tools to engineer AI systems. Moreover, we acquire all of the above-mentioned AI engineering practices. Such practices deliver a foundation for decision-makers to navigate those transformations to create authorized, feasible and extensible systems. We develop and utilize such systems to define better-codified data management engineering tools and practices.  

Contact us now to know more about the technologies we use and the services we provide.