Machine Learning: A Math Problem or a Workflow Problem?

For good reason, machine learning has a highly technical focus. But less talked-about challenges lie in managing the human capital and workflows associated with the tech.


As machine learning (ML) use-cases expand to include building risk models and trading algorithms, and to finding connections in a fog of data, capital markets firms are also experiencing growing pains when it comes to building some semblance of structure around what can be largely experimental artificial intelligence (AI) projects.

This is true of many jobs that require specific skillsets, but it’s common for a talented machine-learning engineer to get sidetracked with issues that don’t involve

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact or view our subscription options here:

You are currently unable to copy this content. Please contact to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Waterstechnology? View our subscription options

Waters Wrap: When looking for tech & data jobs, be curious

Senior executives across the industry tell Anthony that while having the right technical skills as a programmer or data specialist is important, the most desired qualities in new hires are curiosity and the ability to ask good questions.

You need to sign in to use this feature. If you don’t have a WatersTechnology account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here