Remove Experimentation Remove Measurement Remove Publishing
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What you need to know about product management for AI

O'Reilly on Data

You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. It may even be faster to launch this new recommender system, because the Disney data team has access to published research describing what worked for other teams.

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Do You Need a DataOps Dojo?

DataKitchen

Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. A centralized team can publish a set of software services that support the rollout of Agile/DataOps. Central DataOps process measurement function with reports. They also can provide education and training enterprise-wide.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

To achieve this, EUROGATE designed an architecture that uses Amazon DataZone to publish specific digital twin data sets, enabling access to them with SageMaker in a separate AWS account. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog. This process is shown in the following figure.

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How Block is accelerating engineering velocity through developer experience

CIO Business Intelligence

This article goes behind the scenes on whats fueling Blocks investment in developer experience, key initiatives including the role of an engineering intelligence platform , and how the company measures and drives success. Rather, Coburns team optimizes for fast experimentation and a metrics-driven approach.

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Methods of Study Design – Experiments

Data Science 101

Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV. Reliability: It means measurements should have repeatable results. For eg: you measure the blood pressure of a person.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. Companies like Google [2], Amazon [3], and Microsoft [4] have all published scholarly articles on this topic.

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Drug Discovery Needs AI To Discover More Treatments

Smart Data Collective

The greatest advantage of AI is that it can digest vast amounts of medical knowledge — from thousands of published reports and scientific papers, say — and devise novel predictions and formulations that would take human researchers years of inefficient experimentation to find. AI Casts a Wider Net for Clinical Trial Participants.