AWS Machine Learning

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

Accountability 110

Accountability Government Engineering Scripts 110

Enghouse Interactive

JULY 10, 2018

In the hustling world we live in, the sense of community is exciting in the world of banking. For Partner Colorado, Enghouse’s use of open APIs allows integration with the in-house CRM. Focusing on profit margins isn’t what drives this industry but an all-inclusive “what’s best for the members” approach.

Self service 40

Self service CRM Contact Center Analytics 40

AWS Machine Learning

JUNE 24, 2022

To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and interact; how to separate the concerns, responsibilities, and skills; and how to use AWS services optimally.

Enterprise 95

Enterprise Engineering Accountability APIs 95