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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

Understanding E-commerce Conversion Rates There are a number of metrics that data-driven e-commerce companies need to focus on. It is a crucial metric that provides priceless information about your website’s ability to transform visitors into paying customers. Some of the most important is conversion rates.

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What you need to know about product management for AI

O'Reilly on Data

You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. It may even be faster to launch this new recommender system, because the Disney data team has access to published research describing what worked for other teams.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

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Eight Silly Data Things Marketing People Believe That Get Them Fired.

Occam's Razor

Marketer, is not spent with data you''ll fail to achieve professional success.]. Many used some data, but they unfortunately used silly data strategies/metrics. And silly simply because as soon as the strategy/success metric being obsessed about was mentioned, it was clear they would fail. You'll get fired.

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Unintentional data

The Unofficial Google Data Science Blog

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation. And for good reason!

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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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The AIgent: Using Google’s BERT Language Model to Connect Writers & Representation

Insight

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. Data Collection The AIgent leverages book synopses and book metadata.