A working AI model is built on solid, reliable, and dynamic datasets. Without rich and detailed AI training data at hand, it is certainly not possible to build a valuable and successful AI solution. We know that the project’s complexity dictates, and determines the required quality of data. But we are not exactly sure how much training data we need to build the custom model.
There is no straightforward answer to what the right amount of training data for machine learning is needed. Instead of working with a ballpark figure, we believe a slew of methods can give you an accurate idea of the data size you might require. But before that, let’s understand why training data is crucial for the success of your AI project.
The Significance of Training Data
Speaking at The Wall Street Journal’s Future of Everything Festival, Arvind Krishna, CEO IBM, said that nearly 80% of work in an AI Project is about collecting, cleansing, and preparing data.’ And he was also of the opinion that businesses give up their AI ventures because they cannot keep up with the cost, work, and time required to gather valuable training data.
Determining the data sample size helps in designing the solution. It also helps accurately estimate the cost, time, and skills required for the project.
If inaccurate or unreliable datasets are used to train ML models, the resultant application will not provide good predictions.
7 Factors That Determine The Volume Of Training Data Required
Though the data requirements in terms of volume to train AI models is completely subjective and should be taken on a case by case basis, there are a few universal factors that influence objectively. Let’s look at the most common ones.
Machine Learning Model
Training data volume depends on whether your model’s training runs on supervised or unsupervised learning. While the former requires more training data, the latter does not.
Supervised Learning
This involves the use of labeled data, which in turn adds complexities to the training. Tasks such as image classification or clustering require labels or attributions for machines to decipher and differentiate, leading to the demand for more data.
Unsupervised Learning
The use of labeled data is not a mandate in unsupervised learning, thus bringing down the need for humongous volumes of data comparatively. With that said, the data volume would still be high for models to detect patterns and identify innate structures and correlate them.
Variability & Diversity
For a model to be as fair and objective as possible, innate bias should be completely removed. This only translates to the fact that more volumes of diverse datasets is required. This ensures a model learns multitudes of probabilities in existence, allowing it to stay away from generating one-sided responses.
Data Augmentation And Transfer Learning
Sourcing quality data for different use cases across industries and domains is not always seamless. In sensitive sectors like healthcare or finance, quality data is scarcely available. In such cases, data augmentation involving the use of synthesized data becomes the only way forward in training models.
Experimentation And Validation
Iterative training is the balance, where the volume of training data required is calculated after consistent experimentation and validation of results. Through repeated testing and monitoring
model performance, stakeholders can gauge whether more training data is required for response optimization.
How To Reduce Training Data Volume Requirements
Regardless of whether it’s the budget constraint, go-to-market deadline, or the unavailability of diverse data, there are some options enterprises can use to reduce their dependence on huge volumes of training data.
Data Augmentation
where new data is generated or synthesized from existing datasets is ideal for use as training data. This data stems from and mimics parent data, which is 100% real data.
Transfer Learning
This involves modifying the parameters of an existing model to perform and execute a new task. For instance, if your model has learnt to identify apples, you can use the same model and modify its existing training parameters to identify oranges as well.
Pre-trained models
Where existing knowledge can be used as wisdom for your new project. This could be ResNet for tasks associated with image identification or BERT for NLP use cases.
Real-world Examples Of Machine Learning Projects With Minimal Datasets
While it may sound impossible that some ambitious machine learning projects can be executed with minimal raw materials, some cases are astoundingly true. Prepare to be amazed.
| Kaggle Report | Healthcare | Clinical Oncology |
| A Kaggle survey reveals that over 70% of the machine-learning projects were completed with less than 10,000 samples. | With only 500 images, an MIT team trained a model to detect diabetic neuropathy in medical images from eye scans. | Continuing the example with healthcare, a Stanford University team managed to develop a model to detect skin cancer with only 1000 images. |
Making Educated Guesses
There is no magic number regarding the minimum amount of data required, but there are a few rules of thumb that you can use to arrive at a rational number.
The rule of 10
As a rule of thumb, to develop an efficient AI model, the number of training datasets required should be ten times more than each model parameter, also called degrees of freedom. The ’10’ times rules aim to limit the variability and increase the diversity of data. As such, this rule of thumb can help you get your project started by giving you a basic idea about the required quantity of datasets.
Deep Learning
Deep learning methods help develop high-quality models if more data is provided to the system. It is generally accepted that having 5000 labeled images per category should be enough for creating a deep learning algorithm that can work on par with humans. To develop exceptionally complex models, at least a minimum of 10 million labeled items are required.
Computer Vision
If you are using deep learning for image classification, there is a consensus that a dataset of 1000 labeled images for each class is a fair number.
Learning Curves
Learning curves are used to demonstrate the machine learning algorithm performance against data quantity. By having the model skill on the Y-axis and the training dataset on the X-axis, it is possible to understand how the size of the data affects the outcome of the project.
The Disadvantages of Having Too Little Data
You might think it is rather apparent that a project needs large quantities of data, but sometimes, even large businesses with access to structured data fail to procure it. Training on limited or narrow data quantities can stop the machine learning models from achieving their full potential and increase the risk of providing wrong predictions.
While there is no golden rule and rough generalization is usually made to foresee training data needs, it is always better to have large datasets than suffer from limitations. The data limitation that your model suffers from would be the limitations of your project.
What to do if you Need more Datasets
Although everyone wants to have access to large datasets, it is easier said than done. Gaining access to large quantities of datasets of quality and diversity is essential for the project’s success. Here we provide you with strategic steps to make data collection much easier.
Open Dataset
Open datasets are usually considered a ‘good source’ of free data. While this might be true, open datasets aren’t what the project needs in most cases. There are many places from which data can be procured, such as government sources, EU Open data portals, Google Public data explorers, and more. However, there are many disadvantages of using open datasets for complex projects.
When you use such datasets, you risk training and testing your model on incorrect or missing data. The data collections methods are generally not known, which could impact the project’s outcome. Privacy, consent, and identity theft are significant drawbacks of using open data sources.
Augmented Dataset
When you have some amount of training data but not enough to meet all your project requirements, you need to apply data augmentation techniques. The available dataset is repurposed to meet the needs of the model.
The data samples will undergo various transformations that make the dataset rich, varied, and dynamic. A simple example of data augmentation can be seen when dealing with images. An image can be augmented in many ways – it can be cut, resized, mirrored, turned into various angles, and color settings can be changed.
Synthetic Data
When there is insufficient data, we can turn to synthetic data generators. Synthetic data comes in handy in terms of transfer learning, as the model can first be trained on synthetic data and later on the real-world dataset. For example, an AI-based self-driving vehicle can first be trained to recognize and analyze objects in computer vision video games.
Synthetic data is beneficial when there is a lack of real-life data to train and test your trained models. Moreover, it is also used when dealing with privacy and data sensitivity.
Custom Data Collection
Custom data collection is perhaps ideal for generating datasets when other forms do not bring in the required results. High-quality datasets can be generated using web scraping tools, sensors, cameras, and other tools. When you need tailormade datasets that enhance the performance of your models, procuring custom datasets might be the right move. Several third-party services providers offer their expertise.
To develop high-performing AI solutions, the models need to be trained on good quality reliable datasets. However, it is not easy to get hold of rich and detailed datasets that positively impact outcomes. But when you partner with reliable data providers, you can build a powerful AI model with a strong data foundation.
Do you have a great project in mind but are waiting for tailormade datasets to train your models or struggling to get the right outcome from your project? We offer extensive training datasets for a variety of project needs. Leverage the potential of Shaip by talking to one of our data scientists today and understanding how we have delivered high-performing, quality datasets for clients in the past.
