Data Labeling

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

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

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

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

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

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.

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

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

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.

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

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

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.

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

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.

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.

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.