CloudFactory Blog

3 key takeaways from our latest LinkedIn Live event where we explored the state of AI & the importance of integrating human intervention with automation.

Healthcare providers face mounting pressure to increase clinical efficiency and improve patient outcomes. The need for automation and AI is now.

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.

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

Keep these 6 important features of data annotation tools in mind to find the right fit for your AI and machine learning project.

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. How you are processing data that powers AI 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.