Remove Data Collection Remove Metrics Remove Modeling
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This should not be news to you.

Metrics 157
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Excellent Analytics Tip #26: Every Critical Metric Should Have A BFF!

Occam's Razor

But the problem is that single golden metrics hide valuable insights and, more often than not, drive bad behavior. Here's my proposal: If you are pushed to have a single golden metric, give it a partner. The BFF metric you find should not be one that is very far away. So, great metric. Honestly, who can blame them.

Metrics 160
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Email Marketing: Campaign Analysis, Metrics, Best Practices

Occam's Razor

The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. You just have to have the right mental model (see Seth Godin above) and you have to… wait for it… wait for it… measure everything you do! Just to ensure you are executing against your right mental model.

Metrics 138
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From project to product: Architecting the future of enterprise technology

CIO Business Intelligence

Understanding and tracking the right software delivery metrics is essential to inform strategic decisions that drive continuous improvement. They achieve this through models, patterns, and peer review taking complex challenges and breaking them down into understandable components that stakeholders can grasp and discuss.

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What you need to know about product management for AI

O'Reilly on Data

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

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Practical Skills for The AI Product Manager

O'Reilly on Data

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

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Managing risk in machine learning

O'Reilly on Data

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.