Remove Deep Learning Remove Measurement Remove Metrics
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New Deep Learning Systems Profoundly Disrupt Fleet Management Operations

Smart Data Collective

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. One of the biggest applications of this technology lies with using deep learning to streamline fleet management. Route adjustments made in real time.

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Moving from Red AI to Green AI, Part 1: How to Save the Environment and Reduce Your Hardware Costs

DataRobot Blog

Machine learning, and especially deep learning, has become increasingly more accurate in the past few years. In the graph below, borrowed from the same article, you can see how some of the most cutting-edge algorithms in deep learning have increased in terms of model size over time.

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What you need to know about product management for AI

O'Reilly on Data

Measurement, tracking, and logging is less of a priority in enterprise software. Many consumer internet companies invest heavily in analytics infrastructure, instrumenting their online product experience to measure and improve user retention. These companies eventually moved beyond using data to inform product design decisions.

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What are model governance and model operations?

O'Reilly on Data

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. A catalog of validation data sets and the accuracy measurements of stored models.

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

Corinium

A properly set framework will ensure quality, timeliness, scalability, consistency, and industrialization in measuring and driving the return on investment. It is also important to have a strong test and learn culture to encourage rapid experimentation. Build multiple MVPs to test conceptually and learn from early user feedback.

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Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

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Data Scientist’s Dilemma – The Cold Start Problem

Rocket-Powered Data Science

If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data. The objective function (also known as cost function, or benefit function) provides an objective measure of model performance.