Some say, “Data is the new oil,” or “Extra is always a plus.” Then, you’ll hear some say, “Small data is the next big thing.” So you wonder, “Who should I follow?” Truth is, neither is right nor wrong.
First, rather than worry about the amount of data your select AI model will need, focus on assessing the significance of the problem you want to solve.
After gaining a deep understanding of that meaningful problem, use this guide to confidently determine how much data your AI model needs. And, I’ve also covered what to do when your datasets are limited. Explore!
How Much Data Does AI Need? Explained in Detail
Note that the process of collecting, cleaning, and labeling AI data costs money and time. Whether you plan to collect the data from the source or use existing data, spending money and time is universal.
So, besides guiding you on how to determine how much data AI needs, I’ll also include pointers to optimize spending. Here’s how you determine how much data you really need to make AI work.
1. Reassess your goal
Reference the documentation you’d prepared while assessing the significance of the problem you want to solve. How complex is the problem at hand?
Generally, many do agree that most simple problems require basic AI systems. However, there’s a catch here!
While a problem may be simple, the model you select to solve it may be data hungry. So, the next thing to do after evaluating problem complexity is to select an AI model while considering how data efficient the model can get.
Lastly, ensure you put together a data requirements document. This document should define what separates quality from unfit data.
Remember, the quality of the data matters more than the volume when getting data for AI. Also, it is cost-efficient to work with existing models, unless you really need to build one from scratch.
2. Establish a data volume baseline
Besides the complexity and data requirements of your select model, other factors such as acceptable error margin and input diversity impact how much data AI needs. So, what can you do to lower the stress of considering these factors when determining AI data volume?
Start small. Yes, instead of waiting to collect a huge amount of data, use a modest dataset to build a prototype and evaluate its performance.
Approach an ai expert to review the AI model’s documentation to determine the least amount of data your model can work with.
If you don’t have access to an expert, use the documentation and proven approximation techniques like the rule-of-thumb approach or statistical methods.
Sometimes, you’d even realize that a model that was speculated to need a lot of data works fine with the modest dataset, saving you time and resources.
3. Train and evaluate for generalization before expanding the dataset
With an approximation of how much data you need, proceed to collect and preprocess the data. Then, train and evaluate your model’s output or performance.
The goal is to assess how well your model performs when presented with unseen data.
If you notice the AI struggles when presented with unseen data, this is a sign of overfitting. This means the model has not seen enough diverse data to generalize well.
Other signs that your model needs more data include biased predictions, high results variance, underrepresented classes, and low accuracy even when presented with simple tasks.
Using the data from your findings, gradually add more data, focusing on addressing the model’s weaknesses and filling other gaps identified during the testing phase.
In certain instances, you may run into a data availability roadblock. This is to be expected, especially when you are working on a model that requires sensitive data, rare or unique data, or costly data. Here’s a solution.
What to Do When Your Datasets Are Limited?
1. Augment your current dataset
Dataset augmentation is the process of creating variations of the data within that dataset. For instance, if you are working with images, adjust brightness, crop, or rotate them to create new variations or examples.
Apart from images, you can also augment text by using the synonyms of text or paraphrasing text. For audio, you could change the speed or pitch. All these approaches focus on reducing overfitting and enhancing your model’s capabilities.
2. Crowdsource data or use public datasets
To optimize data collection cost, crowdsource data.
Crowdsourcing involves getting data from platforms that outsource AI data collection or labeling tasks to a global taskforce. These platforms include Appen or Amazon Mechanical Turk.
Alternatively, you can tap into publicly available data. Filter through government databases, AI research repositories, or get web data through APIs or source it from AI data providers.
3. Employ synthetic datasets
At times, collecting real data is infeasible. Why? Due to sensitivity levels, privacy, or how expensive it can get. In such situations, you can generate synthetic datasets. You do this with the help of algorithms.
The data generating algorithms are usually based on existing datasets or statistical models. They can be used to simulate real world scenarios, generating scalable and cost-effective data to fill gaps when working with sensitive or rare data.
4. Capitalize on transfer learning
If the other three tactics do not work, opt for transfer learning. This technique involves using a pre-trained model that was initially trained on a large general dataset to build your custom solution.
Simply select a pre-trained model that closely matches your goal and fine-tune it using your limited data. This reduces the need for extensive data and speeds up the training process.
Closing Words
Without a doubt, the effectiveness of AI models relies heavily on the quality and amount of data. While it is easy to evaluate quality, determining how much data AI needs right from the start is not that straightforward.
Each AI model has unique data requirements, and factors like the model’s complexity, error margins, and more impact the amount of data an AI model may need. So, rather than stressing as much, start small, focus on quality, and iterate based on feedback.
