Realize the business impact promised by Machine Learning.
Today’s Business teams face a new challenge: how to achieve operational excellence on the AI and ML assets being generated by their technical teams. Without scalable, uniform deployment processes for the latest open source tooling, and capabilities in place for analytics, teams face the reality that invested dollars will be lost. Read on to learn the basics of what it means to operationalize models, and how FastScore can help teams realize true operational capability for their modeling workflow.
Adopting ML and AI - It’s an organizational challenge
There is a lot of research showing the business benefits from adoption machine learning and AI broadly across any organization. But while it’s easy to say we will leverage our data with open source solutions, it’s not as simple to implement scalable repeatable capability in the organization. Adopting tools and processes that solve for broad technical adoption, and prevent lock in for the long term.
Watch this webinar which talks about the fundamental organization and technical needs for building ML capability across the enterprise.
Operational Excellence for Analytics - what does it mean?
Business teams are used to tracking meaningful metrics that drive profitable outcomes. Talk about market share, inventory turns and pricing elasticity are typical. But in the age of a data driven business, business teams need to look for and implement new metrics. In particular leaders should consider what operation excellence means for their analytic workflows. Teams and processes should be able to measure and improve on analytic focused metrics like SLAs for deployment, model drift, and iteration rates.
Ask this question: is there a single place I can look to understand how all my analytic assets are performing?
Why technical abstractions matter to the business teams.
There is a constant tension in the enterprise to support as many teams and software techniques as possible, while at the same time maintaining operational efficiencies and costs. Business teams should insist that technical architectures today do not limit, as much as possible, future choice. This is where technical abstractions come into play. The technical teams must surface and implement solutions that allow maximum flexibility. In the analytics arena, specific options must be implemented: support for open source languages, ability to leverage cloud computing, and capability to support a multitude of business applications.
Read this blog post that explores things to consider when building out machine learning capability in your organization.