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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. 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.

Marketing 362
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Do You Need a DataOps Dojo?

DataKitchen

Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. For example, some teams may recognize services revenue in the quarter booked, and others may amortize the revenue over the contract period. Develop/execute regression testing . Agile ticketing/Kanban tools. Deploy to production.

Metrics 243
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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. That metric is tied to a KPI. But it is not routine.

Metrics 156
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Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. There are fat books to teach you how to experiment ( or die! Insights worth testing. What does a robust experimentation program contain?

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What is a DataOps Engineer?

DataKitchen

DataOps enables: Rapid experimentation and innovation for the fastest delivery of new insights to customers. Instead of focusing on a narrowly defined task with minimal testing and feedback, DataOps focuses on adding value. Create tests. Test data automation – create test data for development on-demand.

Testing 152
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Higher-ed CIOs embrace academia’s AI challenges

CIO Business Intelligence

We developed a model to predict student outcomes based on metrics from historical evidence,” he says. “We We’ve done a lot of experimentation on these adaptive tools that use AI,” says Ventimiglia. We ran a pilot with data scientists and other specialists on campus, and using the model, they were able to make very accurate predictions.

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Web Analytics 2.0 Book: In Stores Now!!

Occam's Razor

I am absolutely thrilled that my book Web Analytics 2.0 The waterfall of positive feeling stems from the fact that this book was very hard to write. I only had one job, at Intuit, when I wrote my first web analytics book. The Pitch: I invite you to consider buying my second web analytics book. Request for help.