Data collection is a never-ending topic. If you're thinking about AI for your business, it is the second step after identifying the use case. Most individuals and businesses struggle to build a data set that is ready for AI. This is a major challenge, as artificial intelligence is only as good as the data you train it with.

For AI training, data collection involves acquiring model-specific data that you can annotate and use to train machines. It may sound simple, but there is actually more to it. If you would like to collect data to train your AI, here's everything you need to know. 

Data Collection Strategy

Creating a data-driven AI training system is the hardest part of being an AI specialist. You are likely to encounter many challenges, such as data accessibility and compliance. However, you can always automate the process to develop a clear strategy for successful AI data collection. Here are some tips.

1. Identify Data Sources

To develop datasets for AI training, you need to know where to get the data from. When starting, it's advisable to keep the models simple, so your data sources will play a major role here. These are the most common data sources to collect data from. 

Open Source Datasets

This is the fastest and easiest way to collect data for your AI training. There are thousands of free, easy-to-use, and time-effective open-source datasets that you can find online. Although they may contain huge amounts of detailed data, it is also a disadvantage as you'll have to clean and sort out the data to find useful datasets. It is advisable to rely on this data source, but only from credible service providers.

Build Synthetic Datasets

As the name suggests, synthetic datasets are generated through computer programs. Unlike most sources, they are not made from the documentation of real-world data. So why is it an option? Synthetic datasets are particularly functional if you can't access real-world data. Moreover, it's easy to define features, such as formats, within the synthetic datasets. 

Web Scraping

If you're looking for specific data from the web, you can choose to type it repeatedly or copy and paste it. However, this will take a lot of time as you may be in need of a lot of data. The best solution to this is web scraping. Web scraping tools can extract any kind of information from the internet, plus they can fetch updated data for you.

If you choose to scrape the web for AI training data, ensure that you have the required permissions to avoid running into legal trouble. Alternatively, proxies come in handy for web scraping. Even if you're trying to access data using mobile devices, there are 4G mobile proxies that use gateway software to assign a specific IP to help you scrape the web.  

Manual Data Generation

This is the last option you can explore to get your AI training dataset. As its name suggests, you need to collect data yourself manually instead of automatically. Sometimes the data you need may be in your personal or organization's data lake, but if that's not the case, you need to collect the data through crowdsourcing. Crowdsourcing involves assigning human workers tasks to gather the data that you need. This process is very complex and should be the last resort if you need datasets for your AI training. 

2. Quality, Scope, and Quantity

AI training doesn't just require large datasets. It also requires feeding the system with carefully curated, formatted, and functional data. 

When building a dataset for AI training, you need to aim for data diversity. It would serve you better to get both internal and external data. One of the goals is to build a strong and unique dataset that cannot be copied easily by other users. While it is crucial to have data from various sources, the key to successful AI training is meaningful data that relates to the project at hand. 

Formatting is also essential for reducing the data and trimming it down to suit your specific requirements, especially if you'd like to work on exclusive tasks.

3. Data Preprocessing

When you have a dataset that you view as essential, diverse, and functional for your AI training, the next step is preprocessing. It involves selecting specific pieces of data and grouping similar ones together to build a training set. This is essential for the project as it enables you to filter and sort the data to come up with sharper and more relevant predictions. 

4. Feature Creation

Feature creation involves finding the most useful variables that you'd like to use in the model. It is a subjective process that requires a lot of creativity and personal work. You can create the new features by mixing existing features using subtraction, addition, and ratio. It is especially useful if you'd like to train the model to deal with specifics, such as image or speech data collection. This will help you to avoid feeding incomplete or blurry images to the model and make the models more intuitive as they train. 

5. Public Datasets

As we mentioned earlier, data collection is a key part of the development of any AI-based system. When you gather complete private datasets, they contain the specifics of your field of focus and every relevant aspect that you need to predict success. However, you can also use public datasets for your training. 

Public datasets are products of established businesses and organizations that are generous enough to share. However, the sets only contain general information about training processes in a wide range of areas. Although they won't help much in providing specific data for your model, they can give you an insight into the trends in your niche. 

Collecting data for AI training isn't a straightforward process. It takes time, experience, and commitment to complete. It is also a never-ending process because you must constantly collect the latest data and continue training your model to adapt to the changing times.