Introduction to MLOps

Introduction to MLOps

An overview of MLOps field, its similarities & differences compared to DevOps, & how it can help organizations navigate common issues.

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3 Essentials for Your NLP Data Workforce

3 Essentials for Your NLP Data Workforce

Natural language processing (NLP) is a fast-growing AI technology, but data labeling for NLP is complex. Here is what to look for in an NLP data workforce.

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Doctors Shouldn't Annotate Medical Data. Expert Annotators Should.

Doctors Shouldn't Annotate Medical Data. Expert Annotators Should.

Doctor shortages are impacting healthcare innovators’ ability to label data for computer vision solutions. Outsourcing the work will help.

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5 Key Questions to Ask Before Outsourcing Healthcare Data Labeling

5 Key Questions to Ask Before Outsourcing Healthcare Data Labeling

Computer vision improves patient care, streamlines medical decisions, and lowers costs. Ask these questions before outsourcing healthcare data annotation.

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ICYMI: Key Insights from HITL Expert Robert Monarch

ICYMI: Key Insights from HITL Expert Robert Monarch

Learn 3 key takeaways from our latest LinkedIn Live event where we explored what it takes to combine human and machine intelligence effectively.

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From Data Collection to Adoption: How Humans Help Medical AI Come to Life

From Data Collection to Adoption: How Humans Help Medical AI Come to Life

From data collection to training and deploying working models, the development of medical AI comes with many important challenges and opportunities.

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Optimizing Decision Making by Combining Automation and People

Optimizing Decision Making by Combining Automation and People

An incremental design approach to automation and machine learning affords strategic opportunities for choosing to route exceptions to machines or people.

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4 Reasons You Should Outsource Data Entry

4 Reasons You Should Outsource Data Entry

Data entry is a crucial part of any digital transformation project. Sometimes it makes more sense to outsource than to burden your own team.

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Choosing a Data Labeling Service Part 2: Communication & Quality Control

Choosing a Data Labeling Service Part 2: Communication & Quality Control

How can you determine if a data labeling service will deliver quality work? How they communicate and handle quality control are key indicators.

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Choosing a Data Labeling Service Part 1: Hiring and Vetting

Choosing a Data Labeling Service Part 1: Hiring and Vetting

How can you determine if a data labeling service will deliver quality work? It starts with their vetting, hiring, and training processes.

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What Can We Learn From HR About AI Bias?

What Can We Learn From HR About AI Bias?

People have unconscious biases that affect hiring decisions. People also can hard-code their biases into an AI system. Humans in the loop can help.

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3 Ways Humans in the Loop Add Value to the AI Lifecycle

3 Ways Humans in the Loop Add Value to the AI Lifecycle

Humans play a critical role throughout the AI lifecycle, from data cleaning and labeling to quality control and automation monitoring.

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Accelerating ML Model Development with Human in the Loop

Accelerating ML Model Development with Human in the Loop

Developing ML models requires a lot of data and skilled people to work with it. Here’s our HITL approach for machine learning model development.

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From Meals to Video: Connecting a Remote, Global Workforce

From Meals to Video: Connecting a Remote, Global Workforce

Building and maintaining relationships while working remotely calls for creativity. We use pod socials to connect globally at CloudFactory.

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Five Great Books about AI and Machine Learning

Five Great Books about AI and Machine Learning

Whether you’re a seasoned pro or new to artificial intelligence, continued learning important. Here are a few of our favorite machine learning & AI books.

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Partnering to Achieve an Adaptive Workforce

Partnering to Achieve an Adaptive Workforce

Some organizations are winning in a pandemic by adapting to changing customer and market demands. We’ve provided adaptive workforces for a decade. Here’s what they look like.

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