A carefully formulated training plan that incorporates business goals and technical capabilities makes the difference between success and failure in the marketplace
When beginning a machine-learning project, it’s easy to get excited by the possible successful algorithm that can be brought to your business. Whether it’s an improvement in customer engagement or the ability to analyze how people are talking about your product, there is a range of potential benefits that could make machine learning the investment that defines your year. However, in a world dominated by the hype around AI, it is more important than ever to make sure that your business and technical teams are aligned exactly on which problems you want the project to be solved. In fact, you should precisely document the intended technical skills of your final product. The reason is simple. A clear vision will help you to extract the maximum possible value from the hidden key to machine learning – training.
The training process is an essential element of developing a successful machine-learning model, yet people who are new to AI often don’t pa the attention it deserves. How well you plan the training phase, and the datasets you select for training, can make or break your project. If you thoroughly understand how the training process works and source just the right volume of quality data, you can dramatically improve your project’s ROI.
In this article, we’ll explore the process of developing a machine-learning model. However, before we do that, it’s important to understand the tools we’re working with.
What is AI training data?
Essentially, training data is what you use to teach your algorithm to perform its designed function. When it’s run on your model, training data acts like a collection of examples that your algorithm can return for help when making predictions about new data. Each data point usually consists of an input and a label, where the label provides the answer to the ‘question’ that you want your model to deal with.
While this is a simple concept, the composition of your training data can vary massively depending on your model’s use case. For sentiment analysis, the input could be a tweet or a short review of a product, while the label would classify the input as the positive, negative or neutral sentiment. However, for image recognition, the input could be a picture of an animal with a label denoting a ‘cat’ or a ‘dog.’ Sometimes a simple label isn’t enough to help an algorithm learn quickly, so some forms of training data include richly detailed tags to boost the model’s rate of improvement.
In most cases, it is preferable to have a large amount of training data. However, not every data point performs the same function during the training process. Your overall training dataset should be split into three parts: training data, validation data, and test data. We’ll explain in more detail how these processes work later.
Preparing your data
Since training data is the textbook that your model will learn from, data quality is absolutely crucial. To use the above examples, feeding our sentiment analysis algorithm pictures of pets would probably cripple it beyond repair. While this is an extreme example, your data should have a laser focus on your intended use case: there’s no room for fluff. Before it goes anywhere near your algorithm, make sure that your data is cleaned, appropriately tagged, and highly relevant. It’s also important to have an appropriate volume of training data, since having too few examples would hinder your algorithm’s ability to spot useful trends in the data and improve its accuracy.
Once your data is of a good quality, it should be split randomly into the three different subsets to avoid any implicit bias that could end up affecting your model. As a general rule, the training subset will form about 70-80% of your total data, with the remainder split between the validation and test subsets. At this point, you’re ready to unleash your data on your machine-learning model.
How AI training works
There are three phases that a machine-learning model goes through before it is ready to perform its assigned task. It is often helpful to use an example, so let’s continue with our dataset of dogs and cats, and assume we want to build an algorithm that can recognize these animals in images.
- First, using random variables available to us in the data, we run the model on our data’s training subset, asking it to identify dogs and cats in the images. After checking the results, it is likely that the algorithm will be failed spectacularly. This is because the model has very little idea of how these variables relate to the label.
- Using our results, we’re now able to start adjusting these variables in a way that may improve the algorithm’s accuracy on the next run. In our example, this could involve helping the model to recognize the correlation between different breeds of dog. When we’re satisfied that we’ve improved the model’s understanding of these variables, we can run the data again.
- On the second run, the effect of other variables on the algorithm’s accuracy will become obvious. At this point, we repeat the process a number of times, improving the model in a number of ways as we go. Each of these cycles is called a training step. Once the model is showing significant improvement, it may be ready for validation.
- The purpose of validation is to test our model against new data, while still giving it access to all the tags and labels that help it to make its predictions. Validation data and training data are structured in the same way to give the model the best chance of success. It should do better at this point than when it began training, but there are still a few issues with its predictions.
- One of the key things to look out for when evaluating validation results is overfitting. This happens when the model has been trained to only recognize examples from the training data, rather than learning the trends behind them. Validation also provides an opportunity to uncover new variables that may be affecting the algorithm. In our example, perhaps our algorithm is struggling to categorize images where the animal is partially obscured. We will need to account for this, as well as the adjustment of other parameters, in the next training step.
- With our new variables in mind, we can return to training and continue adjusting and improving the algorithm. Alternatively, if our model has done exceptionally well, we can progress to testing.
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- Testing provides another opportunity to test our model against new data, only this time the data has the labels and tags removed. This allows us to evaluate how the model would perform against real-world data. If the model is accurate during testing, we can be confident in using it for its designed purpose. If not, we use our results to return to training and begin the process again.
Although it may not initially seem like it, training provides a huge opportunity to improve ROI. In exactly the same way that messy data can ruin your product, investment in quality can improve your model by orders of magnitude. As more and more companies begin to dabble in AI, high-quality human-annotated training data is providing the competitive edge that separates the industry leaders from the pack.
However, great training data is a rare commodity that takes time to source or creates. It’s worth taking the time to build a solid plan around your training data and source trustworthy partners who share your vision. When you’re sure that your data is absolutely aligned with your goals, your model will have every chance of outperforming everyone in the marketplace.