Remove Data Collection Remove Experimentation Remove Optimization Remove Testing
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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

There are many ways that data analytics can help e-commerce companies succeed. One benefit is that they can help with conversion rate optimization. Collecting Relevant Data for Conversion Rate Optimization Here is some vital data that e-commerce businesses need to collect to improve their conversion rates.

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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. Without clarity in metrics, it’s impossible to do meaningful experimentation. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 362
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How Svevia connects roads, risk, and refuse through the cloud

CIO Business Intelligence

But today, Svevia is driving cross-sector digitization projects where new technology for increased safety for road workers and users is tested. Taking out the trash Division Drift has been key to disruptively digitize Svevia’s remit with the help of the internet of things (IoT), data collection, and data analysis.

Risk 86
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Next Stop – Predicting on Data with Cloudera Machine Learning

Cloudera

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 first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

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What you need to know about product management for AI

O'Reilly on Data

The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. This has serious implications for software testing, versioning, deployment, and other core development processes.

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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 Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

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. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.

Metrics 156