Remove 2018 Remove Experimentation Remove Modeling Remove Risk
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Machine Learning Product Management: Lessons Learned

Domino Data Lab

Unfortunately, a common challenge that many industry people face includes battling “ the model myth ,” or the perception that because their work includes code and data, their work “should” be treated like software engineering. I was fortunate to see an early iteration of Pete Skomoroch ’s ML product management presentation in November 2018.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%).

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Regulations and compliance requirements, especially around pricing, risk selection, etc., Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. In addition, the traditional challenges remain.

Insurance 250
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Reflections on the Data Science Platform Market

Domino Data Lab

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. Reflections. Jupyter) or IDEs (e.g.,

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A New Era in Data Warehousing

Cloudera

Leading insurers are underwriting policies with lower risks. Data teams in these insurance firms are leading the charge in rebuilding entire business models around data and analytics. When good stuff happens in the background, and we take it for granted, we know the technology behind the scenes is working.

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Six Nudges: Creating A Sense Of Urgency For Higher Conversion Rates!

Occam's Razor

It is 2018—why are there still light gray below-the-fold add to cart buttons? Not easy, but your business model has to change to survive.). Not wanting to risk it, I click on the Find in Store link you see at the bottom of the page. Still, I’m heartbroken that some the simplest elements of ecommerce stink so much.

Strategy 124