Remove 2021 Remove Data Quality Remove Deep Learning Remove Experimentation
article thumbnail

AI Adoption in the Enterprise 2021

O'Reilly on Data

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale. Techniques.

article thumbnail

The DataOps Vendor Landscape, 2021

DataKitchen

Download the 2021 DataOps Vendor Landscape here. DataOps is a hot topic in 2021. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. QuerySurge – Continuously detect data issues in your delivery pipelines. OwlDQ — Predictive data quality.

Testing 300
article thumbnail

AI adoption in the enterprise 2020

O'Reilly on Data

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. It seems as if the experimental AI projects of 2019 have borne fruit. Supervised learning is dominant, deep learning continues to rise.