This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
When combined with artificial intelligence (AI), an interoperable healthcare dataplatform has the potential to bring about one of the most transformational changes in history to US healthcare, moving from a system in which events are currently understood and measured in days, weeks, or months into a real-time inter-connected ecosystem.
These development platforms support collaboration between data science and engineering teams, which decreases costs by reducing redundant efforts and automating routine tasks, such as data duplication or extraction. Store operating platform : Scalable and secure foundation supports AI at the edge and dataintegration.
In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining dataintegrity and security.
Cloud-based data storage solutions, such as Amazon S3 (Simple Storage Service) and Google Cloud Storage, provide highly durable and scalable repositories for storing large volumes of data. The integration of AI and ML into data engineering pipelines enables a wide range of applications. Morgan Kaufmann. Morgan Kaufmann.
There was the team that I was on, where we were very intensely focused on making tools and setting up the environment for development and training for data scientists, as well as helping out with the actual productionization work. They started off as doing dataintegrations, and then became the ML monitoring team.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content