Article: Monetizing your models with machine learning solutions, by Stu Bailey, Contributor, InfoWorld
Over the last year more and more companies have begun to understand the capabilities of artificial intelligence and machine learning and how they impact their ability to run models faster and efficiently to meet market demands.
While integrating machine learning and artificial intelligence products and services in to the overall business process, it is important to give business users the ability to access and run machine learning models on their data anytime.
In this article, we’ll focus on bringing an incremental, agnostic, production-first approach to the design and build of machine learning and AI techniques. These best practices aim to combine open source and commercial tools with existing systems in the enterprise, enabling some core benefits, such as;
- Building an MLaaS tool that quants, analysts and data scientists want to use with minimal overhead.
- Creating a sustainable process for maintaining and expanding the machine learning footprint in the enterprise (e.g., onboarding data, new tools, and lab-to-factory process).
- Generating value from machine learning in a variety of business lines
Implementing machine learning as a service solves a huge problem for companies now. At the end of the day, it is all about the need to quickly create an analytic product that a business can monetize.
Looking for solutions, such as an MLaaS approach, is a step in the right direction to not only build an efficient model, but also create a successful approach to drive return on the data science investment.
