Machine learning (ML) and Artificial Intelligence (AI) are rapidly emerging as transformative technologies that look poised to change the way we live and work.
We stand to create unprecedented levels of abundance in the coming decades (and in ways that we are only beginning to imagine). AI and ML will likely proliferate and transform every industry; this will lead to increased productivity and will enable the development of new products and services that were previously unimaginable.
However, today the AI/ML industry is in nascent stage and has an abundance of inefficiencies. If we are to realize its full potential, we need to overcome challenges such as: a lack of enough skilled talent, ineffective leveraging of rapidly evolving supporting theory and tools, a deficiency of operational best practices (leading to: late, expensive, insecure, unreliable, and untrustworthy AI and ML solutions).
There are many parallels between the challenges Software Engineering faced 15 years ago, and the challenges AI and ML face now. Software Engineering resolved these problems through the emergence of a discipline called DevOps. DevOps is the combination of cultural philosophies, practices, and tools that increases an organization's ability to deliver applications and services at high velocity. The AI and ML industry, taking notes from Software Engineering, the has introduced a new discipline called MLOps in an attempt to add more value at scale. MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. This initiative looks promising.
Overall, machine learning and AI are exciting and powerful technologies that are poised to shape the future in profound ways. While there are challenges to be overcome, we are more than capable of meeting them, and the potential rewards of doing so are immense. My thesis is that MLOps will play a vital role in the future of this industry.