The share of time and work applied to different types of training data services in an artificial intelligence (AI) and machine learning (ML) models accounts for over 70% of the entire project, according to the IBM research information. This increases the demand for dataset collectors and developers such as a text annotator, an image labeler, and a video annotation specialist in the marketplace. Moreover, the global market size of AI training dataset deep learning is projected to cross $4.09 billion by 2028 from just $1.15 billion in 2020 with a staggering growth rate of over 19.5% CAGR during the forecast period, as per research conducted by Research and Markets Inc.

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What Are AI Training Data Sets and Training Data?

Training Data

Any types of data in multiple formats used for building training datasets, which are consumed by the artificial intelligence and machine learning models to develop understanding about the objects, human, animals, and other natural entities as well as textual, visual, and sensual presentations of digital information and making decisions/predictions by the AI/ML projects is known as training data. The training data is processed through multiple stages and activities to build meaningful inputs for machines.  

Training Data Sets 

A data file such as text, video, audio, image, or other sensual presentation processed with annotation, classification, and other tagging to make it consumable for machine learning/artificial intelligence algorithms for building understanding about the real-world scenarios and making suitable predictions and data-driven decisions for any test scenario is called an AI training dataset. The creation of an AI training dataset consists of the collection and cleansing of data gathered from multiple sources, and classifying and annotating those data files with the features of content in that particular file so that it can be fed into the machine learning algorithms.

Importance of Training Data

Training data plays a pivotal role in the entire AI-powered projects. You cannot imagine the very existence of the machine learning field without training datasets. Machines are not able to think, learn, decide, or predict like humans do. We educate them through training datasets in a wide range of forms and formats so that those machines can behave like humans do. Thus, training data is the soul of artificial intelligence and machine learning technology.

What Is AI Data Collection in Artificial Intelligence?

The process of data collection is gathering the real-world information, scenarios, voice and sound data, textual scripts, images, videos, and other types of data from numerous sources of voice, video, text, and image data. There are hundreds of different sources from where data can be collected for building text, video, image, and speech recognition dataset such as:

  • Recorded videos such as road-traffic, aerial view, human body movement, CCTV videos, entertainment video, and many others
  • Hand-written scripts in different languages
  • Printed texts such as documents, tickets, receipts, documents, books, letters, and many others
  • Recorded audios such as conversation, songs, monologues, and different voices
  • Numerous types of images such as human body parts and gestures, fruits, animals, objects, different sceneries, and others  
  • Sensory data such as heat, touch, strike, blow, speed, moist, and others

What Are 4 Major Types of Training Data Used in AI Data Services?

The most common types of training data used in building AI/ML training datasets include the following:

main ai training data types


Text is one of the most basic types of data, which is extensively used in building AI text training data sets for a wide range of projects powered by machine learning and natural language processing (NLP) techniques such as chatbots and others. Text classification training data files include printed and hand-written text in different formats and documents.


Machine learning image training data is another major type of data that is extensively used for building datasets for consumption in artificial intelligence algorithms. The typical use cases of image training datasets include facial recognition and emotion recognition applications.


Audio training data set is built from numerous sources of audio data files such recorded conversations, voices, music, and speeches. The most common use case of audio data in machine learning algorithms include the speech recognition and transcription applications.


Video is a very crucial type of training data that is extensively used for real-time decision making in modern artificial intelligence projects such as automatic vehicles and other. Labeling and storing video training data of higher quality for the use of AI projects training purposes makes your applications very effective.

Top Challenges of Data Collection for Companies in Developing ML Databases

Top challenges encountered by ML database developing companies in collecting data for AI training datasets include:

  • Managing huge volumes of data from multiple sources
  • Converting audio and video data in different languages, accents, dialects, and cultures
  • Accuracy and quality of the data collected from different sources
  • Copyrights of collected data to use for commercial purposes
  • Noises and discrepancies in the training data
  • Data security, regulatory compliances, and others
ai data collection common challenges

How Expert Providers of Data Collection Services for ML Datasets Like Us Can Help You

Using the services of specialized services providers like us for data collection services can assist in building a cross-functional team for professional data collection services such as:

  • Multi-channel and multi-source data collection
  • High quality data collection in different formats
  • Data collection in multiple languages, dialects, accents, and styles
  • High-definition videos recorded to cover many aspects of real-world environments
  • Professional-class images with full rights to use for commercial purposes
  • Faster collection of data while maintaining all quality and legal compliances
  • And much more

Why Should You Outsource the Collection of Datasets for Machine Learning Projects?

According to the Research and Markets projections, the global market size of human resource outsourcing (HRO) is expected to reach $45.8 billion by 2027 from $32.8 billion in 2020 with a consistent growth of over 4.1% CAGR during the forecast period. There are numerous reasons that you should outsource data collection services:

  • Cost-efficiency – You can save substantial cost by outsourcing your data collection process for ML projects
  • Specialized service – Outsourcing service providers specialize in the domain so you get professional-level service
  • Time-saving – By outsourcing data collection, you save reasonable time so that you can focus on other major activities and business ideas.
  • Greater productivity/quality – You can achieve high quality data for ML/AI projects by outsourcing because they are aware of modern technology trends and data quality standards.

Why Choose Us for Training Data and AI Solutions Requirement?

We are a professional company to provide our valued clients with data annotator recruitment services for building high-quality AI training data sets. We are located in Ukraine, which is one of the brightest AI training data outsourcing destinations in the world. You should choose us because we offer great value to your investment and our process goes very simple and straightforward.

Top Values We Offer to Our Clients

Our AI data annotator recruitment services stand out from our competitors due to a range of values we provider to our clients such as:

  • End-to-end solution – We provide comprehensive services that include human resource consultancy, sourcing, hiring, and onboarding the hired candidates. Our team hires specialists that can provide a wide range of annotation services such as polygon, line/spline, semantic segmentation, bounding box annotation, and others perfectly.
  • Fixed and transparent prices – Our pricing scheme is based on high-level transparency and predictability. We offer fixed prices without any hidden charges at all. Our prices are highly competitive to save substantial cost on HR recruitment for our valued clients.
  • Quality of service – Our recruitment services are based on the European and international quality standards that offer greater price/quality ratio as compared to many competitors in the marketplace. We recruit talent whose qualities and skills are 100% matching with the job requirements to create great value for the employers.
  • Professionalism – The people of Ukraine are known for their professional commitment and out-of-the-box approach to solve problems. We recruit highly qualified specialists that provide a high level of proficiency in their jobs.
  • Faster turnaround time – The response time of our services is very quick to fulfil your requirements. Whether you need to build a new team from scratch or a data annotator to expand your team, we accomplish this task within your deadlines.
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How Does Our AI Data Labeling Recruitment Process Work?

Our process for AI data labeling recruitment is very simple and easy to follow based on a few laid-back steps such as:

  1. Get in touch with us with your job requirements and desired business goals to achieve. Our team analyses your requirements and suggests the most suitable HR solution.
  2. Give go ahead for recruitment. We start sourcing, shorting, and interviewing processes while keeping you in loop.
  3. Finalize the desired candidates. Our team completes the job offer and other related processes.
  4. Sign contract with hired candidate. We will onboard your AI data annotator to work dedicatedly on your project under your direct control.
  5. Get in touch with us again when you need our help once more!

If you want to leverage the power of AI training data service outsourcing, get in touch with us to get high-quality AI training data services remotely!