How Natural Language Processing (NLP) is Revolutionizing Financial Services

How Natural Language Processing (NLP) is Revolutionizing Financial Services

From spotting trends in financial markets to empowering better client experiences, natural language processing (NLP) is transforming the finance sector.

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ICYMI: 3 Takeaways from Our Chat with HITL Expert Robert Monarch

ICYMI: 3 Takeaways from Our Chat with 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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5 Ways Artificial Intelligence Is Transforming Fintech

5 Ways Artificial Intelligence Is Transforming Fintech

From fraud detection to automated loan approvals, AI is transforming the world of fintech with smoother client experiences and reduced financial risk.

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The AI & Automation Must-Have: Humans-in-the-Loop

The AI & Automation Must-Have: Humans-in-the-Loop

Humans are necessary while automating decisions and processes with AI, machine learning, and RPA. Experts discuss the need for humans in the loop (HITL).

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3 Examples: Solving Automation and ML Exceptions with Humans in the Loop

3 Examples: Solving Automation and ML Exceptions with Humans in the Loop

Learn how CloudFactory’s managed workforce worked with 3 companies, each with a problem involving data, automation, and/or ML.

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Should You Outsource Data Labeling for NLP?

Should You Outsource Data Labeling for NLP?

NLP is one of the most difficult AI applications to develop and maintain. When you outsource data labeling, make sure you choose the right team.

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OCR and People: Your Dynamic Data-Entry Duo

OCR and People: Your Dynamic Data-Entry Duo

Optical character recognition (OCR) can improve productivity when transcribing text, but people still play a critical role in quality control.

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6 AI Predictions for 2021: A View From the Trenches

6 AI Predictions for 2021: A View From the Trenches

What will 2021 bring to the world of AI and machine learning? CloudFactory CEO and founder Mark Sears shares our predictions.

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A Day in the Life: Mercy Mandela, Delivery Team Lead

A Day in the Life: Mercy Mandela, Delivery Team Lead

CloudFactory’s project managers lead teams that deliver client work. Meet one of our leaders in Nairobi, Kenya, and read about a typical day for her.

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5 Tips for Mastering Data Preparation for Machine Learning

5 Tips for Mastering Data Preparation for Machine Learning

Data preparation is the most time-consuming part of machine learning. Here are a few tips for getting it right.

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AI in Agriculture: How Scaling Data Labeling Keeps Agronomists in the Field

AI in Agriculture: How Scaling Data Labeling Keeps Agronomists in the Field

Agriculture data is complex. Annotating agtech data often requires help from agronomists. We help Hummingbird Tech overcome that AI product development hurdle.

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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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Why Using Data Scientists for Data Labeling is a Big Mistake [Infographic]

Why Using Data Scientists for Data Labeling is a Big Mistake [Infographic]

Your in-house data scientists shouldn't be doing tedious data labeling work for machine learning projects. They should be focusing on more important innovation.

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5 Qualities in Good Data Labeling Vendors [Infographic]

5 Qualities in Good Data Labeling Vendors [Infographic]

Not all outsourced data labeling partners are a good fit for every AI project. Here are 5 things you need to consider before, during, and after vendor evaluations.

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