Melody Ayeli, who reviews AI projects for Toyota’s CIO, shared insights on common AI failure points in a session at AI Summit in San Francisco.
ML Models
Azavea's mission is to create advanced geospatial technology and research for civic and social impact. They interviewed a handful of leading data labeling firms, and studiously ...
Gartner predicts 85% of AI projects will fail. One of the leading reasons is low-quality data labeling. High-performing machine learning algorithms require high-quality data. ...
After a decade of data work, we’ve learned AI development requires a winning combination of people, technology, and processes. Don’t overlook one important step before beginning ...
Your choices about tooling and workforce will be important factors in your success as you design, test, validate, and deploy any ML model.
Crowdsourcing seems to offer a cheap option for training machine learning models, but it’s rarely as inexpensive as it seems. Here are some of the hidden costs of the crowd.
Natural language processing (NLP) is among the fastest growing AI technologies and one of the most difficult to develop. In this article, we'll share lessons learned over 10 years ...
Natural language processing (NLP) is among the fastest growing AI technologies today. It’s also one of the most difficult to develop. This is the first in a two-article series ...
CloudFactory CRO Mike Riegel spoke about machine learning innovation as a panelist for Google I/O Extended, part of a three-day global conference for developers.
