The New AI Factory Model: How to Scale Quality Training Data

A Production Problem (Solved)

When Henry Ford attempted to produce the Model T at a rapid pace and with high quality, he ran into a problem. It was difficult to organize teams of specialized workers to assemble automobiles, and with so many workers needed to scale the process, it was highly inefficient. To make matters worse, late delivery of parts caused pile-ups of workers vying for space to work and delays in production.

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

Google’s three-day I/O’18 conference in Mountainview, Calif., last week brought together developers from around the globe for hands-on learning, discussion with experts, and a look at Google’s latest developer products. The conference also featured Google I/O Extended sessions held in technology hubs across the country, including a panel discussion that featured CloudFactory Chief Revenue Officer Mike Riegel.

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Solving the Dirty Data Problem: Clean-Data Practices for Tech Innovators [Ebook]

Data is today’s gold for businesses, representing huge potential value. But there’s a catch: the data must be uncovered, clean, and structured.

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People in the AI Tech Stack [Infographic]

For all of AI’s promises, we still need people to do a lot of work behind the scenes to make it all possible. People collect, enrich, clean, and prepare data for AI systems to operate accurately and optimally. In fact, data scientists spend countless hours cleaning and combining datasets, a process commonly referred to as “data wrangling.”

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Top Tools and Workforce Tips to Scale Your Video Annotation

“[Video] data annotation is super labor-intensive. Each hour of data collected takes almost 800 human hours to annotate. How are you going to scale that?”

-Sameep Tandon, CEO of Drive.ai, an autonomous car startup in Silicon Valley and CloudFactory client

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Best Practices for Your AI Workforce [SlideShare]

For all of artificial intelligence’s promises, we still need humans in the loop to make it all possible. Humans collect, enrich, clean, and prepare data for AI systems to operate with optimal accuracy.

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The 3 Forces that Brought AI to Life (And Why it’s Only Now Changing the World)

We think of AI as brand-new technology, but is it? If you dig into its history, you’ll find that artificial intelligence has existed for more than 60 years. In fact, the term artificial intelligence was coined in 1956 by computer scientist and professor of mathematics John McCarthy at Dartmouth College.

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Humans in the AI Tech Stack [White Paper]

Artificial intelligence is not new technology but it is just now finally taking off. Why now, and how are businesses using it? What are the challenges to implementation? In our new white paper, Humans in the AI Tech Stack, we explore AI trends, the importance of choosing the right tools, and how to strategically deploy people in your tech-and-human stack.

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Cloud Robotics Expands Machines’ Access to Intelligence

It is amazing how far technology has advanced in the last decade. Internet speeds, computing power, automation, and big data have evolved at an incredible rate. This rapid growth of speed and power has made it possible to create innovative technologies that would have seemed like science fiction just 10 years ago.

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5 Exciting Ways Companies Use Machine Learning

The release of two machine learning (ML) model builders have made it easier for software engineers to create and run ML models, even without specialized training.

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