Apiumhub
JULY 16, 2023
In their thought-provoking presentation titled “Pragmatic Approach to Architecture Metrics” at GSAS’22 organized by Apiumhub , Sonya Natanzon, and Vlad Khononov delivered valuable insights. Consequently, we assess the capacity of architecture to embrace change through various metrics. Whatever that is.”
CIO
APRIL 25, 2024
For example, an early version of the revised job-matching effort was rather, for the lack of a better word, rude. As an example, Bottaro referenced the part of the system designed to understand intent. Those first waves of hype around generative AI didn’t help. Or at least overly blunt.
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Xebia
AUGUST 9, 2023
Table of Contents What is Machine Learning System Design? Design Process Clarify requirements Frame problem as an ML task Identify data sources and their availability Model development Serve predictions Observability Iterate on your design What is Machine Learning System Design?
Tenable
NOVEMBER 25, 2022
Get the latest on the Hive RaaS threat; the importance of metrics and risk analysis; cloud security’s top threats; supply chain security advice for software buyers; and more! . Yes, keeping tabs on, for example, the number of patched systems and the percentage of trained staffers is a good start. What would this look like?
Hacker Earth Developers Blog
JULY 18, 2023
This term covers the use of any tech-based tools or systems designed to understand and respond to human emotions. The kinds of things that count as empathetic technology include: Wearables that use physical metrics to determine a person’s mood. Let’s use chatbots as an example. Customer service chatbots.
Cloudera
DECEMBER 3, 2021
They identified four main categories: capturing intent, system design, human judgement & oversight, regulations. An AI system trained on data has no context outside of that data. System Design. Systems should be designed with bias, causality and uncertainty in mind. Capturing Intent. Model Drift.
O'Reilly Media - Ideas
JULY 28, 2020
The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.
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