Remove Experimentation Remove Reference Remove Testing
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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Without clarity in metrics, it’s impossible to do meaningful experimentation. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 363
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise.

IT 353
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What you need to know about product management for AI

O'Reilly on Data

This has serious implications for software testing, versioning, deployment, and other core development processes. There may even be someone on your team who built a personalized video recommender before and can help scope and estimate the project requirements using that past experience as a point of reference.

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Digital addiction detox: Streamline tech to maximize impact, minimize risks

CIO Business Intelligence

While tech debt refers to shortcuts taken in implementation that need to be addressed later, digital addiction results in the accumulation of poorly vetted, misused, or unnecessary technologies that generate costs and risks. Implement robust testing: As the CrowdStrike incident demonstrated, thorough testing is crucial.

Risk 108
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CBRE’s Sandeep Davé on accelerating your AI ambitions

CIO Business Intelligence

Sandeep Davé knows the value of experimentation as well as anyone. As chief digital and technology officer at CBRE, Davé recognized early that the commercial real estate industry was ripe for AI and machine learning enhancements, and he and his team have tested countless use cases across the enterprise ever since.

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Designing A/B tests in a collaboration network

The Unofficial Google Data Science Blog

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. This simulation is based on the actual user network of GCP.

Testing 58
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.