6 Key Features of Data Annotation Tools [Infographic]

6 Key Features of Data Annotation Tools [Infographic]

Some data annotation tools won't be a good fit for your AI and machine learning project. Keep these six important features in mind as you evaluate tool providers.

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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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Boiling the Ocean: Processing the Data that Powers AI

Boiling the Ocean: Processing the Data that Powers AI

Even in uncertain times, you’re swimming in an ocean of data. If you’re using AI, how you process and use that data will determine the future of your business.

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Top Benefits and Limitations of Auto Labeling

Top Benefits and Limitations of Auto Labeling

The data annotation. One emerging feature is automation, also known as pre-annotation or auto labeling. This article will focus on some of its benefits and drawbacks.

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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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Seizing Your AI Opportunity Requires Quality Data and Partners

Seizing Your AI Opportunity Requires Quality Data and Partners

CloudFactory partner Scientia shares the AI opportunity and the importance of quality data for machine learning.

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Azavea and CloudFactory: Partners on Quality Training Data and Social Impact

Azavea and CloudFactory: Partners on Quality Training Data and Social Impact

Azavea's mission is to create advanced geospatial technology and research for civic and social impact. They interviewed a handful of leading data labeling firms, and studiously ...

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3 Ingredients for Scaling Quality Data Labeling for Machine Learning

3 Ingredients for Scaling Quality Data Labeling for Machine Learning

Gartner predicts 85% of AI projects will fail. One of the leading reasons is low-quality data labeling. High-performing machine learning algorithms require high-quality data. ...

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How to Take the Security Risk Out of Outsourcing Your Data Labeling

How to Take the Security Risk Out of Outsourcing Your Data Labeling

When you have massive data to label for machine learning, it makes sense to outsource it. But what happens when your data is sensitive, protected, or private? Here’s a quick ...

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MissingLink Gathers Leaders to Discuss AI to Improve the World

MissingLink Gathers Leaders to Discuss AI to Improve the World

Deep learning platform MissingLink and the MIT Media Lab brought together AI innovators in Boston last month for a meetup about how AI is changing the world for the better.

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The One Thing You Need Before Starting Any AI Project

The One Thing You Need Before Starting Any AI Project

After a decade of data work, we’ve learned AI development requires a winning combination of people, technology, and processes. Don’t overlook one important step before beginning ...

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5 Strategic Steps for Choosing Your Data Labeling Tool

5 Strategic Steps for Choosing Your Data Labeling Tool

Your choices about tooling and workforce will be important factors in your success as you design, test, validate, and deploy any ML model.

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The 3 Hidden Costs of Crowdsourcing for Data Labeling

The 3 Hidden Costs of Crowdsourcing for Data Labeling

Crowdsourcing seems to offer a cheap option for training machine learning models, but it’s rarely as inexpensive as it seems. Here are some of the hidden costs of the crowd.

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Scaling Quality Training Data: Best Practices for Your Data Production Line

Scaling Quality Training Data: Best Practices for Your Data Production Line

Your training data operations are like assembly lines: data is your raw material, and you have to get it through production steps to structure it for AI. You need skilled people ...

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3 Steps Toward Data Responsibility in the Digital Age

3 Steps Toward Data Responsibility in the Digital Age

Even before the Facebook–Cambridge Analytica story broke, the World Economic Forum proposed the need for a new era of data responsibility. Here’s how we can contribute to a world ...

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CloudFactory’s Mike Riegel Talks Machine Learning at Google I/O Extended

CloudFactory’s Mike Riegel Talks Machine Learning at Google I/O Extended

CloudFactory CRO Mike Riegel spoke about machine learning innovation as a panelist for Google I/O Extended, part of a three-day global conference for developers.

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