White Papers


Migration Guide: Hadoop to Databricks

This comprehensive self-guided playbook will assist you step-by-step with your migration from Hadoop to Databricks.


Why insurance AI requires a training data platform

While insurance companies that employ AI have the potential to develop significant competitive advantages, the costs and uncertainties associated with creating high-quality training data often keep ML models from reaching their full potential.


The Guide to Labeling Automation

Download The Labelbox Guide to Labeling Automation to learn more about why training large models on large datasets without automation is so challenging, why Model Assisted Labeling is the labeling automation strategy proven to reduce time and effort, and real-world use cases.


Training Data Platform Guide

Machine learning teams at insurance companies are leveraging deep learning and convolutional neural network (CNN) models from the large amounts of data they collect for marketing, claims automation, risk assessment, and much more. Data science teams spend a disproportionate amount of their time processing, labeling and augmenting training data. Training data platforms can help free up time so they can focus on building the actual structures which they were tasked to create.


Advance your business with AI and ML

This e-book shows how enterprises across industries are using Red Hat OpenShift to build AI/ML solutions that deliver real business outcomes.


Data Warehouses Meet Data Lakes

Ventana Research found that 73% of organizations are combining their data warehouse and data lakes in some way — and 23% of organizations are replacing the data warehouse with data lakes. As the data warehouse and data lake converge, a new data management paradigm has emerged that combines the best of both worlds: the Lakehouse architecture.


The Outsourcers' Guide to Quality

Like any project or task, without the proper tools, data labeling vendors simply can’t do a good job. Learn tips for evaluating vendor toolsets and our approach to tooling in the Outsourcer's Guide to Quality.


Crowd vs. Managed Team - A Study on Quality Data Processing at Scale

Hivemind data scientists tested CloudFactory’s managed workforce against a leading crowdsourcing platform’s anonymous workers. Completing a series of tasks, from basic to complicated, they determined which team delivered the highest-quality structured datasets and costs associated.


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. But how do you choose the right data labeling vendor? Find all of the answers here.


Foundations for Architecting Data Solutions

Now more than ever, CIOs and COOs must maximize long-term success throughout the life of AI projects. One of the ways of doing that is by reducing risk.


Scaling Quality Training Data

The right workforce gives you the flexibility to respond to changes in the market, products or your business. Find out which workforce is ideal for scaling and accelerating your AI training data labeling.


Accelerate AI With Annotated Data

Discover how 9 industry leading companies are employing data annotation solutions to accelerate their machine learning projects and deliver the true promise of AI.


Reduce Risk & Improve Analytics with Solutions to Real-time KYC Compliance

Leverage our digital identity cloud API Personator to protect against fraud, verify customer data and ensure compliance at point-of-entry. Cross verify all contact information – address, name, email and phone – and SSN and ID documentation with Personator. Try it Free!


GE Aviation: From Data Silos to Self-Service

This white paper tells the story of GE Aviation’s data revolution. Discover the history of their data teams, the technological and organizational setup that enabled transformation, use cases, how they handle data education, and more.


The Importance of AutoML for Augmented Analytics

This white paper provides a deep dive into how AutoML came to be, the difference between it and Augmented Analytics, and how they both have brought about the rise of the citizen data scientist.


Empowering Chief Data Officers With Tools to Succeed

We surveyed more than 50 Chief Data Officers (CDOs) worldwide to uncover how they overcome their data and organizational challenges. This report explores the data landscape and maps the Data Revolution. Learn more.


Six Key Challenges to Building a Successful Data Team

Whether you’re in the process of building a data team from the ground up or looking to scale a data team that already exists, this white paper will detail how to address, avoid, and fix challenges. Learn more.


Data Science Operationalization: Ten Steps

Use this guide to learn how to find the common ground between data and IT teams, empowering them to work together to operationalize data projects - quickly. Get the details behind the ten recommendations to go from data project development to operationalizion. Learn more.


InDepth Report - AI Driving a Radical Reshaping of the Healthcare Industry

Read this In-Depth Report to find out more about the prominent role Artificial Intelligence (AI) is taking in the healthcare industry including medical records management, predictive analytics, early diagnosis, and treatment design. Learn more.