Working AI models are built on robust, reliable, and dynamic datasets. Rich and detailed AI training data It is certainly not possible to build a valuable and successful AI solution at the moment. We know that the complexity of the project dictates and determines the quality of data required. However, we do not know exactly how much training data is needed to build a custom model.
There is no simple answer to what the right amount is. Training data for machine learning It is necessary. Instead of working with rough figures, I believe there are various ways to get an accurate idea of the size of data required. But before that, let’s understand why training data is important for the success of an AI project.
The importance of training data
At the Wall Street Journal’s Future of Everything Festival, IBM CEO Arvind Krishna said that about 80 percent of the work in AI projects is data collection, cleaning, and preparation. He also thought that companies are giving up on AI because they can’t afford the cost, work, and time required to collect valuable training data.
Data Decision Sample size It helps in designing solutions. It also helps in accurately estimating the cost, time and skills required for the project.
If you train your ML model using an inaccurate or unreliable dataset, the resulting applications will not provide good predictions.
7 factors that determine how much training data you need
Data requirements in terms of volume for training AI models are completely subjective and should be considered on a case-by-case basis, but there are some universal factors that objectively influence it. Let’s look at the most common ones.
Machine learning model
The volume of training data depends on whether the model is trained using supervised or unsupervised learning. The former requires more training data, while the latter does not.
Map learning
This involves using labeled data, which adds complexity to training. Tasks such as image classification or clustering require labels or attributes that the machine can decipher and distinguish, which creates a demand for more data.
Unsupervised learning
Since using labeled data is not mandatory in unsupervised learning, the need for relatively large amounts of data is reduced. However, the amount of data will still be large enough for the model to detect patterns, identify innate structures, and make correlations.
Diversity and diversity
In order for the model to be as fair and objective as possible, the inherent bias must be completely eliminated. This simply translates into the need for more volumes of diverse data sets. This allows the model to learn the numerous probabilities that exist, so that it does not produce a one-sided response.
Data Augmentation and Transfer Learning
Sourcing quality data for a variety of use cases across industries and domains is not always straightforward. In sensitive areas such as healthcare or finance, quality data is rarely available. In these cases, data augmentation using synthetic data becomes the only way to train models.
Experiments and Verification
Repeated training is a balancing act, and the required volume of training data is calculated through consistent experimentation and validation of results. Through repeated testing and monitoring.
Model performance allows stakeholders to determine whether more training data is needed to optimize responses.
How to reduce training data volume requirements
Regardless of budget constraints, time-to-market, or lack of diverse data, businesses have several options available to reduce their reliance on massive amounts of training data.
- Data Augmentation – Generating or synthesizing new data from an existing data set is ideal for use as training data. This data is derived from and mimics the parent data, which is 100% real data.
- Transfer learning – involves modifying the parameters of an existing model to perform a new task and run it. For example, if a model has learned to identify apples, you can use the same model to identify oranges by modifying the original training parameters.
- Pre-trained models – You can use your existing knowledge as wisdom for new projects. This could be ResNet for tasks related to image identification, or BERT for NLP use cases.
3 – Real-world examples of machine learning projects using minimal data sets
It may sound impossible that some ambitious machine learning projects can be run with minimal raw materials, but in some cases it is surprisingly true. Prepare to be amazed.
| Kaggle Report | Health Care | Clinical Oncology |
| According to a Kaggle survey, more than 70% of machine learning projects are completed with less than 10,000 samples. | An MIT team trained a model to detect diabetic neuropathy in medical images obtained from eye exams using just 500 images. | Continuing with examples from the medical field, a team at Stanford University successfully developed a model that can detect skin cancer using just 1,000 images. |
make an educated guess
There is no magic number for the minimum amount of data you need, but there are some rules of thumb you can use to come up with a reasonable number.
The Rule of Ten
as Rule of thumbTo develop an efficient AI model, the number of training data sets required should be 10 times larger than each model parameter, also known as degrees of freedom. The ’10’ rule aims to limit variability and increase the diversity of the data. Therefore, this rule of thumb can help you get started with your project by giving you a basic idea of how much data set you need.
Deep Learning
Deep learning methods help develop high-quality models when more data is provided to the system. It is generally accepted that 5,000 labeled images per category is enough to create a deep learning algorithm that can perform on par with humans. To develop a very complex model, at least 10 million labeled items are required.
computer vision
When using deep learning for image classification, there is a consensus that a dataset of 1000 labeled images for each class is a reasonable number.
Learning Curve
Learning curves are used to show the performance of machine learning algorithms against the amount of data. By placing the model skill on the Y-axis and the training data set on the X-axis, you can understand how the size of the data affects the outcome of your project.