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Next Stop – Predicting on Data with Cloudera Machine Learning

Cloudera

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines.

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Of Muffins and Machine Learning Models

Cloudera

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.

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Practical Skills for The AI Product Manager

O'Reilly on Data

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

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Web Analytics: An Hour A Day

Occam's Razor

It has been such an amazing journey to write the book, and for it to come up almost exactly a year after I started this blog. Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. There I said it. Damini, Chirag and now the book! :).

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

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Dear Avinash: Attribution Modeling, Org Culture, Deeper Analysis

Occam's Razor

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. That means: All of these metrics are off. This is exactly why the Page Value metric (in the past called $index value) was created. "Was the data correct?" Hopefully soon!

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10 Fundamental Web Analytics Truths: Embrace 'Em & Win Big

Occam's Razor

In this blog post let me share with you some ground truths from my own humble experience. Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. You'll have no time for data analysis, certainly not for data actioning. ~ Likely not.

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