Companies across numerous industries are taking on machine learning projects to improve organizational operations and increase business value. This ebook looks at key considerations in eliminating inefficiencies and building ML strategies that lead to positive outcomes.
From the ebook:
Machine learning (ML) remains an area of strong investment these days, as businesses seek to automate operations via intelligent mechanisms that can adjust and adapt as needed. This reduces the need for human intervention—provided the right series of controls are in place.
However, there is no one-size-fits-all approach to adopting machine learning. Most companies and the departments within them approach the concept from different perspectives with an array of various objectives. Some of these objectives are less coherent than others, which produces inefficiencies and unexpected outcomes and may eventually cause a machine learning shakeout.
Before companies take on any machine learning project, they need clear goals so as to establish an effective machine learning strategy that drives real business value.
I spoke to Scott Clark, CEO of SigOpt, a SaaS automation platform organization. He shared ways to eliminate inefficiencies and produce better outcomes through machine learning. Below are excerpts from that interview.
Scott Matteson: What are some examples of daily usage of machine learning?
Scott Clark: On both a consumer and enterprise level, machine learning is utilized a lot more frequently than people realize. That chatbot on eBay’s website asking if you need help with anything? It’s using machine learning to deliver highly interactive customer service. Running late to a meeting and checking the traffic on your GPS? Your navigation service is using machine learning to average daily experiences and estimate areas of congestion.
On a bigger scale, practitioners use machine learning to improve their organization’s services and increase the company’s bottom line. Credit card companies implement the technology to detect fraud, hedge funds use it to improve their algorithmic trading models, insurance companies refine their risk models with machine learning, and more.