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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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Machine Learning Product Management: Lessons Learned

Domino Data Lab

While the talk provides both organizational foundations for machine learning as well as product management insights to consider when shipping ML projects, I will be focusing on the latter in this blog post. These steps also reflect the experimental nature of ML product management. more probabilistic rather than deterministic).

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Some highlights from 2020

Data Science and Beyond

The initial covid-19 lockdown provided me with extra free time to make the measurement and offsetting of Automattic’s emissions from data centre power use happen. I summarised this work in a post on the company’s blog , and discussed it in an interview with PublishPress.

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Frugal AI: Value at Scale Without Breaking the Bank

Dataiku

Enter Frugal AI , a technique that promises the use of less data and less compute power while guaranteeing robustness within the intended field of use for a given AI model.It In this blog series, we will explore Frugal AI from the perspectives of data, design, trust, and sustainability. It's All in a Name.

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Why Nonprofits Shouldn’t Use Statistics

Depict Data Studio

a school district’s worth of students), it’s still unlikely you need statistics, unless you are trying to answer a scientific-type question (and what scientific-type questions nonprofits with a lot of data might ask is for another blog post on another day). Ask us more about Data Teams! We love to talk about them!).

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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.