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The Ultimate Guide to Data Labeling for Machine Learning

The Ultimate Guide to Data Labeling for Machine Learning

Everything you need to know before engaging a data labeling service. Act strategically, build high quality datasets, and reclaim valuable time to focus on innovation. 

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Design & Build

AI teams have to determine what data they have, what they need to acquire, and how to get high quality labels to turn raw data into training data.

20 Critical Questions to Ask Data Labeling Providers

20 Critical Questions to Ask Data Labeling Providers

This workbook covers what you need to ask when you’re looking for a data labeling service.

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4 Essentials for the Data Labeling Pipeline

4 Essentials for the Data Labeling Pipeline

Supervised learning requires a lot of labeled data. Here’s what it takes to design a high-performance data labeling pipeline for machine learning.

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Data Prep: What Data Scientists Wish You Knew

Data Prep: What Data Scientists Wish You Knew

Webinar: Infinia ML shares what data scientists should know about data prep for machine learning and AI, data readiness strategies, and quality training data annotation.

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Deploy & Operationalize

Once your team has passed the proof of concept stage you're ready to scale, but do you have the data quality that you need for the model results that you'd expect?

5 QA Methods to Win the Race to Quality Data

5 QA Methods to Win the Race to Quality Data

Learn about 5 quality assurance workflows for data labeling and their impact on throughput and data quality for machine learning, AI and business data preparation.

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Crowd vs. Managed Team

Crowd vs. Managed Team

Data scientists at Hivemind tested CloudFactory’s managed workforce against a leading crowdsourcing platform workforce to see which team produced better quality training data, and at what cost.

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The Essential Guide to Quality Training Data for Machine Learning

The Essential Guide to Quality Training Data for Machine Learning

Are you training machine learning algorithms or planning to scale your team's training data operations? Here are some tips to ensure your people, processes, and tools produce quality training data.

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Refine & Optimize

Once your model is in production you'll still need to optimize it, to look our for exceptions and to keep an eye on the processes that your model is driving.

How to Keep Your Machine Learning Models Up-to-Date

How to Keep Your Machine Learning Models Up-to-Date

No matter how robust your initial training may be, keeping your machine learning models up-to-date is essential. Here are two retraining approaches.

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Data Annotation Tools for Machine Learning (Evolving Guide)

Data Annotation Tools for Machine Learning (Evolving Guide)

Selecting the right tool to annotate your data can save you time, money, and frustration. This guide will help choose the best data annotation tools.

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Disrupting the Legal Space with AI

Learn how CloudFactory helped Heretik train their AI models and disrupt the legal industry by streamlining the contract review process.

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Questions about training data or want to learn how CloudFactory can help lighten your team’s load?

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