Enable javascript in your browser for better experience. Need to know to enable it? Go here.
Veröffentlicht : Apr 13, 2021
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Apr 2021
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

All major cloud providers offer a dazzling array of machine-learning (ML) solutions. These powerful tools can provide a lot of value, but come at a cost. There is the pure run cost for these services charged by the cloud provider. In addition, there is a kind of operations tax. These complex tools need to be understood and operated, and with each new tool added to the architecture this tax burden increases. In our experience, teams often choose complex tools because they underestimate the power of simpler tools such as linear regression. Many ML problems don't require a GPU or neural networks. For that reason we advocate for the simplest possible ML, using simple tools and models and a few hundred lines of Python on the compute platform you have at hand. Only reach for the complex tools when you can demonstrate the need for them.

Radar

Download Technology Radar Volume 25

English | Español | Português | 中文

Radar

Stay informed about technology

 

Subscribe now

Visit our archive to read previous volumes