Everyone knows and understands the tremendous scope of the evolving AI market. That is why businesses today are eager to develop their apps in AI and reap its benefits. However, most people don’t understand the technology behind AI models. It requires the creation of complex algorithms that use thousands of trained data sets to build a successful AI app.
The need to use the right AI training data to build AI apps is still understated. Business owners often consider developing AI training data as an easy job. Unfortunately, finding relevant AI training data for any AI model is challenging and needs time. Generally, there are 4 steps involved in the process of acquiring and evaluating the right AI Training Data:
Defining the Data
It usually defines the type of data you wish to input into your AI application or model.
Cleaning the Data
It is the process of removal of unnecessary data & coming to a conclusion whether more data is required?
Accumulating the Data
This is the actual data you collect manually or programmatically for your AI application.
Labelling the Data
At last, the collected data is labelled to be accurately supplied to the AI model during the training phase.
AI training data is crucial for making an accurate and successful AI application. Without the right quality training data, the developed AI program will lead to false and inaccurate outcomes, eventually leading to the model’s failure. Hence, avoiding using bad-quality data for your programs is necessary as it may lead to
- Higher maintenance needs and costs.
- Inaccurate, slow, or irrelevant outcomes from your trained AI model.
- Bad credibility for your product.
- Higher wastage of financial resources.
Factors to Consider When Evaluating Training Data
Training your AI model with bad data is certainly a bad idea. But, the question is how to evaluate the bad and right AI Training Data. Various factors can help identify the right and wrong data for your AI application. Here are some of those factors:
Data Quality and Accuracy
Foremostly, the quality of data you would use for training the model should be given the highest importance. Using bad data to train the algorithm leads to data cascades (substandard effects in the development pipeline) & inaccuracy in the results. Therefore, always use high-quality data that can be identified as- Collected, stored, and responsibly used data.
- Data that produces accurate results.
- Reusable data for similar applications.
- Empirical and self-explanatory data.
Representatives of the Data
It is a known fact that a dataset can never be absolute. However, we must aim at developing diverse AI data that can effortlessly predict and provide precise results. For instance, if an AI model is made to identify people’s faces, it should be fed with a substantial amount of diverse data that can deliver accurate results. The data must represent all the classifications provided to it by the users.
Diversity and Balance in the Data
Your datasets must maintain the right balance in the amount of fed data. The data provided to the program must be diverse and collected from different geographies, from both males and females speaking different languages and dialects, who belong to different communities, income levels, etc. Not adding diverse data usually leads to overfitting or underfitting your training set.It means the AI model will either get too specific or be unable to perform well when provided with new data. Hence, always make sure to have conceptual discussions with examples about the program with your team to get the needed results.
Relevance to the Task at Hand
Lastly, to attain good training data, ensure the data is relevant to your AI program. You only need to gather data that is directly or indirectly related to your task at hand. Collecting unnecessary data with low application relevance may lead to inefficiencies in your application.
Foremostly, the quality of data you would use for training the model should be given the highest importance. Using bad data to train the algorithm leads to data cascades (substandard effects in the development pipeline) & inaccuracy in the results. Therefore, always use high-quality data that can be identified as
Your datasets must maintain the right balance in the amount of fed data. The data provided to the program must be diverse and collected from different geographies, from both males and females speaking different languages and dialects, who belong to different communities, income levels, etc. Not adding diverse data usually leads to overfitting or underfitting your training set.
Lastly, to attain good training data, ensure the data is relevant to your AI program. You only need to gather data that is directly or indirectly related to your task at hand. Collecting unnecessary data with low application relevance may lead to inefficiencies in your application.
[Also Read: What Is Training Data in Machine Learning]
Methods for Evaluating Training Data
To make the right data selection for your AI program, you must evaluate the right AI training data. This can be done by
- Identifying High-Quality Data with Enhanced Accuracy:
To identify good-quality data, you must ensure that the provided content is relevant to the application context. In addition, you need to figure out if the gathered data is redundant and valid. There are various standard quality tests that the data can be passed through, such as Cronbach’s alpha test, gold set method, etc., which can provide you with good quality data. - Leverage Tools for Evaluating Data Representatives and Diversity
As mentioned above, diversity in your data is the key to achieving the needed accuracy in your data model. There are tools that can generate detailed projections and track data results at a multi-dimensional level. This helps you identify if your AI model can distinguish between diverse data sets and provide the right outputs. - Evaluate Training Data Relevance
Training data must only contain attributes that provide meaningful information to your AI model. To ensure the right data selection, create a list of essential attributes your AI model should understand. Make the model familiar to those data sets and add those specific data sets to your data library.
How to Choose the Right Training Data for your AI Model?
It is evident that data is supreme when training your AI models. We discussed early in the blog how to find the right AI training data for your programs. Let us take a look at them:
- Data Defining: The first step is to define the type of data you need for your program. It segregates all the other data options and directs you in a single direction.
- Data Accumulation: Next is to gather the data that you are looking for and make multiple data sets from it which is relevant to your needs.
- Data Cleaning: Then the data is thoroughly cleaned, which involves practices like checking for duplicates, removal of outliers, fixing structural errors, and checking for missing data gaps.
- Data Labelling: Finally, the data that is useful for your AI model is labelled properly. Labelling reduces the risk of misinterpretation and provides better accuracy to the AI training model.
Apart from these practices, you must consider a few considerations when dealing with limited or biased training data. Biased data is AI-generated output based on erroneous assumptions that are false. There are ways like data augmentation and data markup that are incredibly helpful in reducing bias. These techniques are made for regularizing the data by adding slightly modified copies of existing data and improving the diversity of data sets.
[Also Read: How much is the optimum volume of training data you need for an AI project?]
Conclusion
AI training data is the most important aspect of a successful AI application. That is why it must be given utmost importance and significance while developing your AI program. Having the right AI training data ensures that your program can take many diverse inputs and still generate the right results. Reach out to our Shaip team to learn about AI training data and create high-quality AI data for your programs.
