White Papers


The Big Book of Data Engineering

This comprehensive eBook showcases data engineering best practices on the Databricks Lakehouse Platform. You‘ll learn how to translate raw data into actionable data — armed with data sets, code samples and best practices from leaders and experts.


The Big Book of Data Science Use Cases

This comprehensive reference guide provides everything you need to get started with data science at scale — including code samples, notebooks and use cases from leading companies such as Comcast, Regeneron and Nationwide.


The Big Book of Machine Learning Use Cases

The world of machine learning is evolving so quickly that it’s challenging to find real-world use cases that are relevant to what you’re working on. That’s why we collected these technical blogs from industry thought leaders with practical use cases you can leverage today. This how-to reference guide provides everything you need — including code samples and notebooks — to start putting the Databricks platform to work.


Building the Data Lakehouse

Bill Inmon, widely considered the father of the data warehouse, heralds the birth of the data lakehouse, ​​which makes efficient ML and business analytics possible directly on data lakes. According to Bill, the data lakehouse presents an opportunity similar to the early years of the data warehouse market. The lakehouse’s unique ability to combine the data science focus of the data lake with the analytics power of the data warehouse — in an open environment — will unlock incredible value for organizations.


Should I build or buy a training data platform?

Until recently, most ML teams have had to build their own labeling tools or adopt a hodgepodge of open-source tools or service-based labeling workflows. Now however, a new category of tooling has emerged: the training data platform (TDP). TDPs offer best-in-class technology — and are purpose built for AI teams — to combine data, people, and processes into one seamless training data creation experience, enabling ML teams to produce performant models faster and more efficiently.


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.



Delta Lake: The Definitive Guide

Want to learn how to overcome key data reliability challenges? Download a preview of the O’Reilly ebook, Delta Lake: The Definitive Guide, to learn about Delta Lake basic operations and how the time travel feature gives you access to historical data.


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.