Machine learning might be the world’s most important general-purpose technology, but it’s notoriously difficult to launch. Outside of Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption.
Copyright: hbr.org – “Getting Machine Learning Projects From Idea To Execution”
This article presents a six-step practice for ushering machine learning projects from conception to deployment. This disciplined approach serves both sides: It empowers business professionals and it establishes a sorely needed strategic framework for data professionals.
Humanity’s latest, greatest invention is stalling right out of the gate. Machine learning projects have the potential to help us navigate our most significant risks — including wildfires, climate change, pandemics, and child abuse. It can boost sales, cut costs, prevent fraud, streamline manufacturing, and strengthen health care.
But ML initiatives routinely fail to deliver returns — or fail to deploy entirely. They stall before deploying, and at great cost. One of the major issues is that companies tend to focus more on the technology than how it should deploy. This is like being more excited about the development of a rocket than its launch.
In this article, I offer an antidote: a six-step practice for ushering machine learning projects from conception to deployment that I call bizML. This framework is an effort to establish an updated, industry-standard playbook for running successful ML projects that is pertinent and compelling to both business professionals and data professionals.
Shifting a Misplaced Focus — from Technology to Deployment
ML’s problem is in its popularity. For all the hoopla about the core technology, the gritty details of how its deployment improves business operations are often glossed over. In this way, ML is now too hot for its own good. After decades of consulting and running ML conferences, the lesson has sunk in.[…]
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Read more: www.hbr.org
Machine learning might be the world’s most important general-purpose technology, but it’s notoriously difficult to launch. Outside of Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption.
Copyright: hbr.org – “Getting Machine Learning Projects From Idea To Execution”
Humanity’s latest, greatest invention is stalling right out of the gate. Machine learning projects have the potential to help us navigate our most significant risks — including wildfires, climate change, pandemics, and child abuse. It can boost sales, cut costs, prevent fraud, streamline manufacturing, and strengthen health care.
But ML initiatives routinely fail to deliver returns — or fail to deploy entirely. They stall before deploying, and at great cost. One of the major issues is that companies tend to focus more on the technology than how it should deploy. This is like being more excited about the development of a rocket than its launch.
In this article, I offer an antidote: a six-step practice for ushering machine learning projects from conception to deployment that I call bizML. This framework is an effort to establish an updated, industry-standard playbook for running successful ML projects that is pertinent and compelling to both business professionals and data professionals.
Shifting a Misplaced Focus — from Technology to Deployment
ML’s problem is in its popularity. For all the hoopla about the core technology, the gritty details of how its deployment improves business operations are often glossed over. In this way, ML is now too hot for its own good. After decades of consulting and running ML conferences, the lesson has sunk in.[…]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Read more: www.hbr.org
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