Watch our on-demand and register for upcoming webinars to dive deeply into the future of work, AI creation, training data, machine learning and AI tools, crowdsourcing, outsourcing, data production, and data science.

Building Your Next Machine Learning Data Set

Gathering an initial data set for your machine learning project is the first hurdle on the path to a successful ML algorithm. CloudFactory and Keymakr discuss the attributes of an ideal data set, the pros and cons of using a pre-created data set, and best practices for building your own.

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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.

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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.

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What You Need to Know Before Hiring a Data Labeling Service

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.

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Win the Race to Market: How to Accelerate Data Labeling and DL Training

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.

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

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.

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