Importance of Datasets in Machine Learning and AI Research: An Overview


Machine learning and artificial intelligence are two most talked about topics in technology these days. From self-driving cars to GPT-3 and recommendation engines, the applications of these technologies are endless. Given these insights, it has become all the more important to handle huge volumes of data in a way that contributes to AI research. 

If you are a machine learning services company, you already know how significant a role quality and quantitative datasets play in building robust machine learning solutions. Read further as we take this discussion forward. 

What is a Dataset?

A dataset, as the name suggests, is a collection of various types of data, such as textual data, image data, and sensor data, stored in a digital format. These datasets can be used by a machine learning services company to make predictions based on historical data. It can be used to solve challenges, such as image/video classification, face recognition, sentiment analysis, emotional classification, and speech analytics, among others. 

A dataset is generally classified into three segments: training data, validation data, and testing data. Almost 60% of the data in a dataset is used to train the model, the validation data helps check accuracy, and testing data is used to evaluate the performance of the model. 

Datasets for your machine learning model can be acquired from numerous resources. Some of them are open datasets, government data portals, finance & economic datasets, computer vision datasets, NLP (Natural Language Processing) datasets, and more. 

Remember, you can also create a custom dataset by collecting multiple datasets.  

Importance of a Dataset

In addition to enabling you to train machine learning models, datasets also provide you with a benchmark that lets you measure the accuracy of those models. The quality of data that is being fed to a machine learning model is of utmost importance as any model is only as good as the data it is trained on. You cannot expect impressive results when you feed low-quality data to a machine learning model. 

A 2020 report, The State of Data Science, highlighted how most data scientists and AI developers spend over 70% of their time analyzing the datasets, establishing data preparation as the most time-consuming process of a machine learning project lifecycle. Other processes in the lifecycle, such as model training, selection, deployment, and testing, took significantly less time. 

What Makes a Good Dataset?

For any machine learning model to function properly, the quality of dataset is as important as the quality. While it is true that you need enough data for an algorithm to train an algorithm properly, the quality cannot be compromised as that would mean running the risk of overfitting your machine learning model. Two key factors that you should consider when collecting data are relevance and coverage. Also, data scientists recommend using live data when feasible to eliminate the possibility of problems related to bias and blind spots. 

A good dataset, typically, has four features: quality, quantity, scalability, and usability. 

Use cases for Machine Learning Datasets

Different types of datasets are used for different types of machine learning solutions. Let us try to understand the use cases of some of these: 

  • Text datasets are largely used in applications, such as chatbots and sentiment analysis, that require natural language processing. 
  • Audio datasets are particularly useful in computer vision, speech recognition, sound modeling, and more. 
  • Video datasets can be used in creating advanced video production software, such as 3D rendering, motion tracking, etc. 
  • Image datasets can be used in image compression and recognition, speech synthesis, and other operations. 

Handling Data Efficiently for your Machine Learning Project

Once you acquire data or datasets for delivering AI ML services, here are certain tips that might come in handy when dealing with them. 

  • Make sure all data, including input and output variables, is labeled properly. 
  • Don’t use unrepresentative samples for training as that may affect the results adversely. 
  • Train your models effectively by using multiple datasets instead of sticking to just one. 
  • Make sure the datasets you choose are relevant to your problem domain for maximizing efficiency. 
  • Pre-processing your data will make it ready for all the modeling purposes. 
  • One needs to be very careful when selecting a machine learning algorithm as not all dataset types are compatible with all kinds of algorithms. 

Final Thoughts

The importance of machine learning and artificial intelligence has grown immensely over the last few years, and the bar is going to be raised higher as these technologies become mainstream. In order to get the most out of these, it is vital you start with finding a good dataset and database. The quality of data can single-handedly define the trajectory of your tasks.  

Considering how important a role a dataset plays, you might want to put in those extra hours to extract the maximum benefits.