IDG Contributor Network: Monetizing your models with machine learning solutions | Tech News
Over the last few months, I’ve discussed key themes and topics around analytic deployment solutions and technologies. And there’s a reason why understanding these deployment techniques and trends is so important: The more companies, scientists, technologists, etc., begin to understand the capabilities of AI and machine learning, the more challenges they will encounter making sure the analytics developed are able to run faster and efficiently to meet market demands.
Connecting the business to the science is a necessary step for companies as they look to compete and extend their leadership position leveraging machine learning and artificial intelligence products and services. But how can they go a step beyond these point techniques and truly integrate them to overall business process?
One route to success prioritizes the idea of developing a machine-learning-as-a-service (MLaaS) offering. Much like analytic deployment technology itself, MLaaS solutions can bring data scientists, IT and analytic operations together to deploy and scale more models, more often, with better quality and greater efficiency.
It’s all about giving business users (or generally non-data scientists) the ability to access and run machine learning models on their data, while enabling the data science community to quickly iterate, manage, and deploy models for the business to use. The overall intent is to enable the entire organization to monetize the outputs of the data science investment.
The foundation of most successful solutions, those which exhibit true adoption and monetization of analytics, stems from the fact that it’s an integrated solution. In particular, the adoption of machine learning algorithms and techniques is driving a new level of scale required from these systems, which can be challenging to integrate.
An optimal solution should focus on brining an incremental, agnostic, production-first approach to the design and build of machine learning and AI techniques, which aims to combine open source and commercial tools with existing systems in the enterprise. Achieving this type of system enables some core benefits, such as:,
- Creating 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
Here’s an example of how this would work. Let’s suppose Andy and Shawn on your data science team create a great model for predicting whether or not someone will respond to marketing materials, based on their LinkedIn profiles. With an MLaaS solution in place, they would be able to quickly release the model to the system as a deployed asset. Then, the business team could hit a web portal to access the live model and run new predictions on customer data populations of interest, which in turn helps prioritize targets for a marketing campaign or sales outreach.
This solves a huge problem for companies now: The models that the data science team produces are not easily consumable by people elsewhere in the organization.
Many different languages and techniques can be used for a wide range of analytic projects. Deployment is, and will increasingly become, an integral part of the analytic model creation workflow. Teams within an organization will continue to use separate languages and data sources, and leverage common test and deployment frameworks, to move models through the organization. It’s imperative to both understand and plan for the real challenges that exist in handoffs between IT and data science teams, and enables the business to leverage the analytics fully.
At the end of the day, it’s all about that need to quickly create an analytic product that a business can monetize. Looking for solutions, such as an MLaaS approach, is a step in that direction to not only build an efficient model, but also create a successful approach to drive return on the data science investment.
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