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? Choosing the wrong partner can lead to project delays, broken algorithms, dirty data, and surprise costs.
- Important questions that you should ask while evaluating data labeling vendors
- How to ensure high-quality labeling, project scalability, predictable pricing, data security, and workflow-specific tooling for your business
- Real-world examples across a variety of use cases
- Red flags to watch for in deliverables, contracts, and more
You can ask any of your burning questions live during the discussion. Plus attendees will get early access to our brand new vendor evaluation workbook and checklist.
Bill Heffelfinger Presenter
Bill Heffelfinger is Principal Solutions Architect at CloudFactory, where he works with technical partners to build relationships, integrations, and joint solutions to support high-quality end-to-end data labeling operations.
Yasmeen Kashef Presenter
Yasmeen Kashef is a Client Success Manager at CloudFactory, where she is a trusted advisor to innovators who are building machine learning models and want to apply human-in-the-loop solutions to scale high quality training data for a competitive advantage.