Artificial intelligence (AI) and machine learning (ML) models are unable to function without data; however, simply feeding them with large amounts of raw data won’t work. Data annotation is a critical step in training and building AI and ML models.1 Without diverse and accurately processed data, AI and ML models simply cannot function, much less function well.
That’s not all. Even seemingly minor errors during annotation could cause the ML model to make false predictions and impact the performance of the algorithm.2 This is why many industries are now relying on experienced data annotation outsourcing companies and service providers to manage their data needs.
Data annotation outsourcing can be daunting. After all, outsourcing is a rigorous process, especially for those doing it the first time. Let’s dive in to learn more about data annotation and the pros and cons of outsourcing data annotation services.
Data annotation is identifying and labeling relevant data to train machine learning models. Data annotation services, typically performed by data annotation companies, include tagging for various formats of data such as image, video, and text.
With the rise of AI, data annotation services have become indispensable for numerous global industries such as healthcare, autonomous vehicles, retail, and consumer technology, to create precise and error-free training data sets for various computer vision and natural language processing models.
Many types of data annotation services are available for different data formats depending on the bespoke and particular needs of the machine learning model and the use case:
Annotating training data for ML models filters out data that may impact the quality of the output. Only high-quality data should be used to train AI and ML models, especially within the realm of retail and eCommerce.
Nowadays, many companies outsource their data needs to third-party vendors (including dedicated data annotation companies) who specialize in customizing data annotation services for projects of all levels and complexities.
Here are some pros and cons of outsourcing data annotation services to an experienced service provider:
It’s essential to address these issues to ensure that the workplace values and welcomes change. A positive work environment starts with happy and secure employees.
Finding the perfect data annotation service provider requires in-depth research and thorough consideration. . You have to be meticulous and consider several factors before making a decision.
Here are some tips to keep in mind when choosing the right data annotation partner:
While numerous third-party vendors are experts in data annotation, not all are suitable for your needs. Finding your best partner isn’t always about the lowest cost or what other benefits they can offer. It’s all about the value they add to your project and your company as a whole.
As the world’s fastest-growing BPO company, we pride ourselves on delivering Ridiculously Good AI services to our clients and partners—all thanks to our dedicated and highly skilled workforce.
We have a pool of 48,700 full-time Teammates and over 70,000 Taskers in our crowdsourcing platform, TaskVerse, who work with Us to create and perform AI and ML solutions specifically designed to cater to our clients’ needs.
From natural language processing to computer vision, we comply with gold-standard processes and data security measures. We’ve worked hard over the years to create processes that ensure the highest standards for both security and accuracy. Quality is the name of our game.
One of our clients called Us to provide our world-class data annotation services—specifically object mapping—in furthering the development of self-driving autonomous vehicles, making them safer for pedestrians, passengers, and drivers alike.
Through our partnership, these goals were met: