Technology

January 27, 2021

Defining The Landscape - AI

Executives at IT companies in the US and other countries need a solid understanding of the investment and technologies necessary to be successful and deliver

Overview 

AI applications take a lot of moving parts when being developed. It takes different technologies, implementing processes, and most importantly talent. Executives need a solid understanding of the investment and technologies necessary to be successful. We hope this will be of assistance when considering application of AI. In this article you should expect to find a very fundamental explanation of AI applications from an operations and executive standpoint.

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Labs & Factories 

Data exploration, experimentation, predictions and prototyping all must be performed in a different environment that normal processes as to not affect normal operations. More agility may be required here, and a separate sand box to try new things is necessary. Rapid changes will be made, and flexibility is key to handle the pace of change associated with AI implementations. 

Factories are the second part of the foundation to which your AI applications can be built. After being developed in a lab, the metrics and analytics side of the function moves to the “Factory”. In this factory, analytics and measurements should be recorded literally at all times - ideally for an extended period of time as to get a good gauge on what’s going on. Factories ensure that the applications reliability, accuracy and scalability are really in place. Errors, unreliability, and maintenance issues should be expected. Your measurements should be robust so as to be able to handle real world randomness and variance.

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Methodology 

Now for the workflow and relevant departments. MLOps is a play off of the term DevOps. MLOps means a machine learning application of AI, rather than Software Development and IT operations. DevOps disseminates production software. In this case we will be deploying machine learning software. 

Some of the objectives of MLOps include tracking analytics, ensuring stability of your models, and using repetition of processes in your methodology. Engineers and data science will be involved in your MLOps. MLOps processes can obviously vary widely, but they ultimately all involve automation.

Roles

Finally let’s touch on who will be involved in the initial planning and ongoing processes. This work can be carried out by cross-functional teams. Some roles you can expect to include: machine learning engineers, data scientists, architects (cloud), and business executives in the group that have experience with data science.

Please visit ArkusNexus AI page for information about how we can help you develop your AI solution via nearshore software development.

Case Study from Arkusnexus
Keith Goatley
Keith is currently Marketing Manager for ArkusNexus and believes in marketing that is customer centric, data driven, digital first, and purpose led.
kgoatley@arkusnexus.com
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