For a model to start recognizing patterns, it has to evolve through multiple stages of dataset iterations. A human can recognize a dog breed by being introduced to it once or a few times. When it comes to an ML model, dozens or even hundreds of images may still not be enough.
An AI model predicts an outcome based off of the training data, without which your algorithms are rendered useless. A similar correlation makes the long-lasting premise, garbage in, garbage out, more relevant than ever.
If you want your data to foster machine productivity and orchestrate high-speed iteration, consider investing extensively in model training. That being said, this post will cover the following:
- What is training data?
- Training data vs. testing data: Why split your data?
- Why is training data important?
- How much training data do I need?
- Where do I source training data from?
- How can I improve the quality of my training data?
- Things to avoid when dealing with training data
- Final thoughts
What is training data?
Training data is exactly what you feed your model with to ensure your algorithm absorbs high-quality sets of samples with assigned relevant classes or tags. The rule of thumbs is that ML models owe much of their accuracy, efficiency, and functionality to the training data. With time your model gets even better at object identification. In this regard, the practice equals the number of images fed into the model to be able to churn out expected results.
Training data vs. testing data: Why split your data?
Training data, however, is not the same as testing data, although both are essential to teaching an ML algorithm to detect patterns. Your data is usually divided into two halves with the ratios of either 80:20 or 70:30. Of course, there are more complex methods of splitting, but let’s focus on these two for now. A part of the data is used to check how the training data affects the algorithm and the end result, commonly referred to as testing data (20 or 30), and the other half (70 or 80) is the actual training data.
Keep in mind that the divided data should be randomized, or else you’ll end up with a faulty system full of blind spots. You may, fairly enough, call us out on this in that random splitting has its own drawbacks, yet random distribution will suffice for preliminary evaluation.
Splitting data into training and testing datasets is not the end of the story. Since the model has to undergo comprehensive training to generate precise results, it will likely come across the same sample data repeatedly. To help your algorithm avoid seeing the same patterns all over and at the same time not mix the training and testing data, you split your training data again for data validation purposes. This way, you’ll facilitate faster improvement of model performance with the minimized quantity of blind spots, if at all.
To better comprehend the splitting cycle, check out the chart below:
Why is training data important?
At this point, you should have an understanding of what training data is and how it is distinguishable from testing data. Now, why is training data important?
Once and for all, without training data, the machine won’t have information to rely on to deliver proper results. In other words, your model has to know what to look for in a particular dataset. Again, this requirement is met by training data.
How much training data do I need?
Apparently, this is not the response you are looking for, especially if you’re about to launch your own AI-powered solution, but every project will require a slightly different amount of data.
There are a few reasons why there is no concrete answer to this question:
- The amount of training data depends on the complexity of your model for the most part.
- Depending on the errors you get, you are likely to retrain your model as you detect recurring blind spots.
- The understanding of the data needed for training a model comes with experience.
The truth is there is no concrete method or formula to measure and determine the adequate amount of data needed for a given project. Now, an experienced ML engineer might propose an algorithm to deduct the original training data volume, which is completely realistic but not always feasible for companies with a tight budget.
Where do I source training data from?
There are numerous sources where you can get your training data, and your choice is mostly determined by the use case of your image annotation and the purpose of the project.
Open source training datasets
Whether image, video, audio, or text, you can totally use the open-source data, yet its accessibility does not necessarily make it beneficial to your project. Be careful with what input into your model anyways, and always check the usage conditions to avoid dealing with additional expenses.
Data scraping is the method of mining data from a variety of sources using a corresponding toolset. The catch with data scraping is the extent to which its use is legal: In other words, you are safe unless the extracted data is for personal use. If you need datasets for commercial purposes, data scraping is a hard no.
Reaching out to the external vendor by far is the most seamless and effective way for training data.
- First off, it cuts you an ample amount of time, which you can use to optimize other elements of the CV cycle.
- The service provider takes on the responsibility of finding datasets that meet your project requirements.
- The service provider makes sure that the datasets provided meet the regulatory guidelines.
The only thing you should be careful about is the price and the details of the deal, but that’s a general precaution for every stage of the project.
How can I improve the quality of my training data?
Training data quality optimization is the cornerstone of your pipeline, as it induces higher success rates in the final AI rollout: At the end of the day, you harvest what you plant your model with.
Especially in supervised ML, the information has to be labeled accurately, and your image class and training data distribution should be balanced for a quality outcome. Moreover, the information provided has to be deprived of any bias in data to ensure consistency and increased precision throughout your pipeline.
Additionally, almost all AI solutions are challenged by the long tail issue, which is representative of defective classification and can only be tackled by extensive sample data. Making sure your model iterates enough characteristics of your long-tail sample is a significant investment in your training data, which often becomes the primary reason why your ML project fails.
The definition of data quality, however, varies from one company to another: For some, it is the detection of mislabeled data, while for others, quality is attributed to the way the data is organized. More often, companies have a pre-established rubric describing what quality data means within the context of their company and ongoing operations.
In any case, beware that model maintenance is an ongoing process and doesn’t stop after you’ve trained the model.
Things to avoid when dealing with training data
Just like for every CV operation, there are certain precautions when dealing with training data, including underfitting and overfitting.
As discussed above, the training data is introduced to the model in batches. Not repeating this process enough will result in underfitting and lower accuracy rates.
Conversely, feeding the machine with data too many times will make it less prone to accurately identifying new patterns when exposed to them. In other words, don’t abuse your data with extremes; otherwise, you’ll have to restart the training.
Now when you’ve trained your model, it’s time to use that 20% or 30% data to test the model on predictions for final tweaks and fine-tuning. Even when you have the best-performing algorithm, your model won’t work the way you expect it with flawed training data. To that end, training data is paramount to your AI model performance.
To wrap up, knowing how to manipulate your training data for proper usage of your AI project will assist you all through the defining steps of your project. We hope this article expands your understanding of training data, its types, and significance to your ML model and gives you invaluable insights to reinforce your CV cycle.