How Much Data is Required for Machine Learning?

Machine learning, a term Arthur Samuel coined in 1959, has entered every industry with promising problem-solving potential. Although it has revolutionized language and sentiment analytics, its effectiveness depends on the training dataset’s quality management. This post will elaborate on how much data is required for machine learning development. 

What is Machine Learning? 

Machine learning (ML) means a computing device can study previously-gathered historical sample data and learn about a concept like humans: through iterations or repetitive exposure. A well-trained ML model can perform more complex tasks. For example, ML helps automate data management services 

It can convert user-generated content into multiple languages, enabling social media users to overcome language barriers. Simultaneously, machine learning can help business analysts, investors, and governments estimate macroeconomic trends through more robust predictive reporting. 

Why Do You Require a Lot of Data for Machine Learning? 

An ML model can generate output for practically-endless possibilities after processing vast databases. The ML algorithm will only be reliable if historical data used in a machine learning model is sufficient or free of data quality issues. 

Likewise, ML models might handle semi-structured and unstructured data involving images, music, videos, or unique file formats. Especially in the case of market research, social listening, and big data, ML professionals require extensive data. 

Otherwise, they will report impractical or skewed insights, wasting the client organization’s time and resources. Reputable data analytics solutions implement several safeguards to prevent these unwanted outcomes. 

Besides, several ML-based software applications receive mixed reviews once they generate logically-absurd or controversial outputs. Therefore, you want to train the ML model as much as possible so that it will successfully respond to every user query. 

How Much Data is Required for a Machine Learning Project? 

Experienced ML developers recognize that a project’s data requirements depend on the scope, expected accuracy, and intended task complexity. Moreover, leveraging an exceptional data lifecycle management (DLM) approach will enhance dataset quality. So, a project will achieve better outcomes using less extensive training data. 

For instance, training ML to do linear tasks having a few variations to respond to predictable changes in a workflow can require 1000 historical observations. The simpler the activity, the less data you will need. 

Conversely, if you want an ML model to detect the language and expressed emotions in texts without human supervision, assume there is no limit to how many readings you will need to develop this model. 

At least a million records are necessary to depict an individual feature in your ML model for advanced tasks. However, the following principles will help you estimate how much data you need for your machine learning use case. 

Considerations When Calculating Data Required for an ML Project 

1. Inference space influences how much data you need to arrive at a generally-valid conclusion from a much narrower data point collection. For example, describing the growth of bacteria in one pond will need less data. However, using similar observations to estimate how bacteria might grow in every pond worldwide will necessitate vast databases. 

1. The signal-to-noise ratio has often helped sound engineers evaluate audio quality. And it appears in machine learning as the ratio between the contribution of relevant data “signal” and data’s obstructive or distracting properties. If the gathered data is 100% relevant to a use case, less data will be enough for ML operations. However, this idea is an ideal. In practice, always expect some noise to reduce the efficiency of ML model training. 

1. The preliminary regression-led analysis will have low data demand. However, integrating an artificial neural network (ANN) implies you must invest more in big data adoption. 

1. The law of large numbers, or LNN, builds the foundation of probability and statistics. According to LNN, the mean of a larger observation set is more accurate in every situation. If the available resources permit, include as many observations per ML feature as realistically viable. 

Conclusion 

Developing a machine learning algorithm and training the ML models requires financial and technological resources. Additionally, you want to hire domain experts knowing the nuances of big data, automated processes, and data quality management (DQM). 

If misdirected efforts shape the ML implementation, an enterprise will likely lose more resources instead of sharpening its competitive edge via analytics. As such, managers must transparently interact with established ML and analytics providers to forecast the data requirements for an in-house machine learning project.