Best Practices for Adaptive Outsourcing

Continuing Business Operations in a PandemicBest Practices for Adaptive Outsourcing

CloudFactory has been securely delivering work remotely for the better part of a decade. In this guide, our experts share best practices that will help you evaluate an outsourcing provider for their ability to continue business operations when conditions change.

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Growing Your Platform Business With Outsourcing

Are you interested in growing your platform’s user base, cleaning up your database, or innovating through automation and AI? This guide explains how outsourcing can help accelerate your platform growth.


Data Engineering, Preparation, and Labeling for AI 2019

The big challenge for organizations looking to make use of advanced machine learning is getting access to large volumes of clean, accurate, complete, and well-labeled data to train ML models. AI analyst firm Cognilytica explores the race to usable data and evaluates the requirements for solutions that clean, augment, and annotate data for AI.


Data Prep: What Data Scientists Wish You Knew

CloudFactory’s Paul Christianson and Infinia ML data scientist Ben Schneller discuss what data scientists wish you knew about preparing your data for AI projects, data readiness strategies, and high-quality training data annotation at scale.


5 QA Methods to Win the Race to Quality Data

The end goal for every data labeling project is quality data - but how do you get there? There are several QA workflow types but each has pros and cons when it comes to the quality and speed of data outputs. In this panel discussion, we will explore 5 quality assurance workflows for data labeling including tooling, staffing, and how each workflow affects throughput and data quality.


The Outsourcers' Guide to Quality

Data quality is vital when creating reliable algorithms. For companies looking for a solution that equals the quality of their in-house team, it can seem as though outsourcing is an impossible option. Have no fear. This eBook will help you predict the level of quality you can expect from a data labeling provider.


Crowdsourced Workers vs. Managed Workers

Data scientists at Hivemind created 3 identical tasks and hired two teams to complete them. How did crowdsourced workers and a managed workforce differ in terms of accuracy, speed, and effective cost?


5 Qualities in Good Data Labeling Vendors

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


Why Using Data Scientists for Data Labeling is a Big Mistake

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. How much might it cost for a data scientist to annotate a one-hour video?


20 Critical Questions to Ask Data Labeling Providers

When you’re creating high-performing machine learning models, you need quality, labeled data...and lots of it. Getting it can be a challenge. A growing number of innovators are outsourcing data labeling operations so their teams can focus on strategy and innovation. Choosing a data labeling partner is an important decision that can affect your model performance and speed to market. Here’s what you need to ask when you’re looking for a data labeling service.


In-House vs. Managed Workforce Data Labeling Partner

It takes a lot of time and resources to prepare and label data. Learn why outsourcing the data preparation to a managed workforce partner is a good business decision and how in-house responsibilities will shift when you outsource the work.


3 Signs a Data Labeling Provider will Deliver Quality Data

The people, processes, and tools used by outsourced data labeling partners make a big difference in final data quality. Here are 3 signs that you'll receive quality work from your data partners.


6 Ways Some Data Labeling Providers Put Your Data Quality at Risk

The level of data quality you'll receive from data labeling providers depends on several workforce, QA, and tooling factors. Here are 6 ways *some* data labeling providers put your data quality at risk.

Case Study

Medical Image Tagging Made Easier

Medical AI company stays ahead of the curve by labeling 24,000 images in 6 months.

Case Study

Helping an Insurer Compete and Grow

An insurance company expanded their business with the help of a dedicated cloud team that handles upfront data processing needs.


Choosing the Right Data Labeling Partner

It takes a mountain of data to train, test, and build machine learning algorithms and AI projects. You may be considering hiring a data labeling service to take the burden off your in-house data scientists and machine learning engineers. But what does that entail? Watch the webinar to learn what you need to know before hiring a data labeling service.


Win the Race to Market

Developing high-performance deep learning models for computer vision requires a strategic combination of people, tools, and processes in pre-production. Watch the webinar to learn how to streamline your data labeling and experimentation process to accelerate your ML training and your time to market.

Case Study

Financial Documents Transcription

Financial services company leveraged crowd labor to improve turnaround time from days to just minutes with 99% accuracy.


Crowd vs. Managed Team

Data scientists at Hivemind tested CloudFactory’s managed workforce and a leading crowdsourcing platform’s anonymous workers to complete a series of the same tasks, ranging from basic to more complicated, to determine which team delivered the highest-quality structured datasets and at what relative cost.


Scaling Quality Training Data

If you need people to process some portion of the big data that feeds your artificial intelligence, you need a reliable workforce. You’re not alone: more businesses are using in-house staff, contractors, and crowdsourcing to get this kind of work done, and industry analysts expect that trend to increase significantly over the next two years.

Case Study

Ibotta Saves Money for Shoppers

Ibotta finds the people and technology to scale a data-verification process during the busiest retail season of the year.


Anonymous Crowd vs. Managed Team

Data-science tech developer Hivemind designed a quantitative experiment to determine which type of workforce completed a series of increasingly complex tasks to deliver the highest-quality structured datasets. Watch the webinar to learn which workforce performed better.


Humans in the AI Tech Stack

Artificial intelligence is finally taking off. Why now, and how are businesses using it? What are the challenges to implemention? In this white paper, we explore AI trends, the importance of choosing the right tools, and how to strategically deploy people in your tech-and-human stack.


Solving the Dirty Data Problem

Data is today’s gold, representing huge potential value for businesses. By analyzing data, you can discover patterns that can help you make smarter decisions, improve products, and disrupt entire industries. Download our guide to solve the dirty data problem.


The On-Demand Blueprint

Hiring on-demand is the new way of getting work done. You might be playing catch-up later if you don’t participate now. Learn how businesses are leveraging the on-demand economy as a strategic competitive advantage.


An Impact Sourcing Primer

Outsourcing has been a business strategy for as long as entrepreneurs have found alternatives that are less expensive than doing everything themselves. And offshoring has been a part of those same entrepreneurs’ vocabularies for the last 20 years. Impact Sourcing? Well, that’s a newer one.