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Data Engineering, Preparation, and Labeling for AI 2019

Data Engineering, Preparation, and Labeling for AI 2019Getting Data Ready for Use in AI and Machine Learning Projects

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.

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workbook

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.

webinar

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.

webinar

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.

report

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.

report

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.

whitepaper

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.

webinar

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.

webinar

The New AI Factory Model Part II

How do you build a data production line? Watch the webinar to hear from data science and AI workforce experts about how to build your AI production line for high-quality data processing at scale.

webinar

The New AI Factory Model Part I

What is the AI production line? Watch the webinar to hear from experts in technology and people operations who are transforming the way data is processed and structured for AI and machine learning algorithms.

whitepaper

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.

ebook

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.

success story

Ibotta Saves Money for Shoppers

With a fast-growing user base for its mobile shopping app, Ibotta needed people and technology to scale a data-verification process during the busiest retail season of the year.

ebook

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.

success story

Census Records Transcription

Spokeo is on a mission to reunite friends and family and empower users to discover information about their online footprints. To take their product to the next level they decided to add 200 Million census records to it’s people search engine. But how?

ebook

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.

success story

Financial Documents Transcription

Most financial services companies have an automated data capture solution in place, but yesterday's systems weren't built for today's customer-centric business models.

success story

Medical Record Transcription

CloudFactory's medical form processing solutions enables healthcare and medical service providers to find the balance between high accuracy and a suitable document throughput rate while minimizing costs.