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Software Platforms for AI and Machine Learning

Rice University_Brandon Martin_083021A
[Rice University - Brandon Martin]


AI Engineering

Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer. However, developing AI/ML systems has presented several engineering problems that are different from those that arise in, non-AI/ML software development. AI engineering is an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts. 

In contrast to the prevalent rush to develop capabilities and progress individual tools, AI Engineering asks a different set of questions: How can AI help humans achieve mission outcomes? What are the limits of AI systems in practice today? How can we ensure that ethical standards are upheld as AI systems are deployed? 

The rise in availability of computing power and massive datasets have led to the creation of new AI, models, and algorithms encompassing thousands of variables and capable of making rapid and impactful decisions. Too often, though, these capabilities work only in controlled environments and are difficult to replicate, verify, and validate in the real world.


- The Main Challenges

This section aims to investigate how software engineering (SE) has been applied in the development of AI/ML systems and identify challenges and practices that are applicable and determine whether they meet the needs of professionals. Also, we assessed whether these SE practices apply to different contexts, and in which areas they may be applicable. 

The main challenges faced by professionals are in areas of testing, AI software quality, and data management. The contribution types of most of the proposed SE practices are guidelines, lessons learned, and tools.


[More to come ...]



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