The success of machine learning projects relies on the quality of data available to train, test, and validate the models. All significant machine learning challenges can be traced down to the quality and quantity of data available or the lack of it. Getting our hands on the data is the first and the easiest of tasks. Then comes the challenge of labeling, annotating, and structuring the data. The best data labeling happens when combining two resources = highly trained, skilled, and experienced data labelers + smart data labeling tools. 1 - Outsourced data labeling: Hire temporary data labelers and manage them remotely. 2 - Crowdsourced data labeling: Hire data labelers through a crowdsourcing platform. 3 - Partnered data labeling: Hire a data labeling company that offers human and software resources to train your data. This post will guide you through the pros and cons of each data labeling option.
1 - Outsourcing your data labelingHere you remotely hire data labelers individually and form a data labeling team. There are many cases where organizations hire data labelers from developing countries where labor is cheap. The challenge here is to train and manage this workforce yourself, where project managers are responsible for interviewing, hiring, training, and preparing the workers to label data. Pros of outsourced data labeling You can achieve high-quality labeling if you can manage the labeling process successfully. Cons of outsourced data labeling This approach is considerably higher priced than crowdsourcing and contractors.
2 - Crowdsourcing your data labelingHere you hire experienced data labelers through a crowdsourcing platform and assign tasks to all labelers at once. Pros of crowdsourced data labeling Crowdsourced data labeling is ideal for simple data labeling tasks with well-defined naming standards and where there is very little room for labeling challenges. Crowdsourced data labeling tends to cost less, and you can get a lot of data labeled in a quick time. Cons of crowdsourced data labeling The quality of data labeling is something crowdsourced labeling cannot guarantee. Also, as you share data with workers who are not under your supervision or bound by security contracts, there is no guarantee of your data being confidentiality protected.
3 - Data labeling partnerThe spurt of machine learning solutions has given birth to an entire industry that caters to the data labeling demand. Data labeling companies take complete responsibility to hire, train, and employ skilled data labelers and often have access to the latest, most sophisticated data labeling tools. Data labeling companies offer data-labeling-as-a-service, where you can bind these companies with data security and confidentiality protocols and get your data labeled quickly and securely. Data labeling partners often work as your extended teams while taking away people and project management off your plate. The best part of hiring a data labeling partner is that they will charge based on their output volume and manage all aspects of data labeling for you. Data labeling companies are quick to adhere to your communication standards and make data labeling as frictionless as possible. Pros of hiring a data labeling partner
- They can operate as an extension of your team which enables you to ask questions, provide feedback, and communicate efficiently.
- Since they work from multiple locations, they could be ramping up faster and providing services over vacations.
- Transparent, pay-for-what-you-get pricing models ensure that you never overpay for data labeling. It saves your team from managing the data labeling effort.