In order to build intelligent applications capable of understanding, machine learning models need to digest large amounts of structured training data. Gathering sufficient training data is the first step in solving any AI-based machine learning problem.

Data collection means pooling data by scraping, capturing, and loading it from multiple sources, including offline and online sources. High volumes of data collection or data creation can be the hardest part of a machine learning project, especially at scale.

Furthermore, all datasets have flaws. This is why data preparation is so crucial in the machine learning process. In a word, data preparation is a series of processes for making your dataset more machine learning-friendly. In a broader sense, data preparation also entails determining the best data collection mechanism. And these techniques take up the majority of machine learning time. It can take months for the first algorithm to be constructed!

Why is data collection important?

Data collection allows you to capture a record of past events so that we can use data analysis to find recurring patterns. From those patterns, you build predictive models using machine learning algorithms that look for trends and predict future changes.

Predictive models are only as good as the data from which they are built, so good data collection practices are crucial to developing high-performing models. The data needs to be error-free and contain relevant information for the task at hand. For example, a loan default model would not benefit from tiger population sizes but could benefit from gas prices over time.

How much data do you need?

This is an interesting question, but it has no definite answer because “how much” data you need depends on how many features there are in the data set. It is recommended to collect as much data as possible for good predictions. You can begin with small batches of data and see the result of the model. The most important thing to consider while data collection is diversity. Diverse data will help your model cover more scenarios. So, when focusing on how much data you need, you should cover all the scenarios in which the model will be used.

The quantity of data also depends on the complexity of your model. If it is as simple as license plate detection then you can expect predictions with small batches of data. But if are working on higher levels of Artificial intelligence like medical AI, you need to consider huge volumes of Data.

Process of Data Collection

Type of Data Requirements

Text Collection

In different languages and scenarios, text data collection supports the training of conversational interfaces. On the other hand, handwritten text data collection enables the enhancement of optical character recognition systems. Text data can be gathered from various sources, including documents, receipts, handwritten notes, and more.

Audio Collection

Automatic speech recognition technologies must be trained with multilingual audio data of various types and associated with different scenarios, to help machines recognize the intents and nuances of human speech. Conversational AI systems including in-home assistants, chatbots, and more require large volumes of high-quality data in a wide variety of languages, dialects, demographics, speaker traits, dialogue types, environments, and scenarios for model training.

Image & Video Collection

Computer vision systems and other AI solutions that analyze visual content need to account for a wide variety of scenarios. Large volumes of high-resolution images and videos that are accurately annotated provide the training data that is necessary for the computer to recognize images with the same level of accuracy as a human. Algorithms used for computer vision and image analysis services need to be trained with carefully collected and segmented data in order to ensure unbiased results. 

How to Measure Data Quality?

The main purpose of the data collection is to gather information in a measured and systematic way to ensure accuracy and facilitate data analysis. Since all collected data are intended to provide content for analysis of the data, the information gathered must be of the highest quality to have any value.

Regardless of the way data are collected, it’s essential to maintain the neutrality, credibility, quality, and authenticity of the data. If these requirements are not guaranteed, then we can run into a series of problems and negative results

To ensure whether the data fed into the system is high quality or not, ensure that it adheres to the following parameters:

• Intended for specific use cases and algorithms
• Helps make the model more intelligent
• Speeds up decision making 
• Represents a real-time construct

As per the mentioned aspects, here are the traits that you want your datasets to have:

1. Uniformity: Regardless of where data pieces come from, they must be uniformly verified, depending on the model. For instance, when coupled with audio datasets designed specifically for NLP models like chatbots and Voice Assistants, a well-seasoned annotated video dataset would not be uniform.
2. Consistency: If data sets are to be considered high quality, they must be consistent. As a complement to any other unit, every unit of data must try to make the model’s decision-making process faster.
3. Comprehensiveness: Plan out every aspect and characteristic of the model and ensure that the sourced datasets cover all the bases. For instance, NLP-relevant data must adhere to the semantic, syntactic, and even contextual requirements. 
4. Relevance: If you want to achieve a specific result, make sure the data is homogenous and relevant so that AI algorithms can process it quickly. 
5. Diversified: Diversity increases the capability of the model to have better predictions in multiple scenarios. Diversified datasets are essential if you want to train the model holistically. While this might scale up the budget, the model becomes way more intelligent and perceptive.

Choose Right Data Collection Provider

Obtaining the appropriate AI training data for your AI models can be difficult. 

TagX simplifies this procedure by using a wide range of datasets that have been thoroughly validated for quality and bias. TagX can help you construct AI and ML models by sourcing, collecting, and generating speech, audio, image, video, text, and document data. We provide a one-stop-shop for web, internal, and external data collection and creation, with several languages supported around the globe and customizable data collection and generation options to match any industrial domain need.

Once your data is collected, it still requires enhancement through annotation to ensure that your machine learning models extract the maximum value from the data. Data transcription and/or annotation are essential to preparing data for production-ready AI.

Our approach to collecting custom data makes use of our experience with unique scenario setups and dynamic project management, as well as our base of annotation experts for data tagging. And with an experienced end-to-end service provider in play, you get access to the best platform, the most seasoned people, and tested processes that actually help you train the model to perfection. We don’t compromise on our data, and neither should you.

www.tagxdata.com