This article first appeared on Snapt.
Implementing a machine learning (ML) model in an application is only a small part of the journey. When you’ve done all the research, determined that ML is a good fit for your use case, and chosen the correct ML model and tooling, you still need to ensure that you handle your data and training correctly.
To help you on your journey, this guide highlights a few common pitfalls you should avoid when preparing data and training your ML model.
And while avoiding these mistakes is no guarantee that your ML journey will be a successful one, it will certainly help you determine more quickly whether your ML model is for you and save significant time in the process.
1. Don’t Use Unverified And Unstructured Data
One of the most common mistakes ML engineers make in AI development is to use unverified and unstructured data.
Machine learning engineers might make this mistake for all sorts of reasons: lack of understanding, lack of training, or even laziness. Whatever the reason, failure to properly verify and structure your data can critically undermine your project.
Unverified data might have errors, such as duplication, conflicting data, lack of categorization, and other issues that could create anomalies during the training process.
So even though it might seem like a painful process that will slow down your development, it is crucial that engineers invest time in working through the data carefully and ensuring that the data being collected is relevant to the ML learning and structured correctly to ensure the ML tool can correctly interpret the data.
2. Don’t Label Your Datasets Incorrectly
Voluminous data can be overwhelming even for a powerful computer system. It’s important that our datasets are correctly labeled to help systems make sense of the data and structure the learning effectively.
This is another task that can be quite labor-intensive and often not given as much attention as it needs. It’s important that ML engineers prepare their datasets extensively before starting to train the ML model.
Your ML training will only be as successful as the data you feed it, so it’s important that you put in the work to prepare data that sets you up for success.
3. Don’t Train Your Model With Small Datasets
We’ve already spoken about how unverified and unstructured data can throw challenges at your ML model. Well, similarly, insufficient data is also likely to limit the learning of your ML tool, though it does depend on the intention you have for your data. The deeper the learning model, the more data is required.
Therefore, ensure that you have access to the right amount of unique data before trying to implement your ML solution.
4. Don’t Reuse The Same Dataset Over And Over
Companies sometimes have access to large volumes of data that can be used for ML. Still, ML engineers need to be careful not to reuse the same data sets in the hope it will lead to more accurate modeling, as the opposite will likely be true.
Using the same data—and therefore the same learnings—in another area of work could lead to data biases and repetitiveness in the inference, which may restrict the accuracy of your data.
Artificial intelligence systems learn from past datasets to predict answers in new data. Using the same training data repeatedly on AI-based models or applications could lead them to be biased and derive results that are a product of their previous learning. So, when working with your ML models, it’s important to constantly provide new data to help drive better results and understanding.
5. Don’t Allow Your Model To Become Biased
Machines are not inherently biased or prejudiced in their decision-making. However, if an ML model is fed biased data, it’s likely the model could produce results that appear prejudiced.
When trying to use certain information, such as age, gender, orientation, income level, etc., ensure that you either have a diversified set of data to work with or rather avoid using it entirely.
Similarly, even if you have a wide variety of diversified data, be wary of any inherent biases that it might contain. Instead, try to modify the data to remove these biases.
The same sort of critical evaluation should take place in the testing process, in which you should look at the different outcomes and testers should throw prejudiced scenarios at the learning models to check whether the learning leads down a biased path.
6. Don’t Rely On Your Model Learning Independently
While we would like to believe that the different ML models we have chosen are capable enough to learn from the data on their own, the reality is that if we don’t carefully monitor the learning process and amend or alter the data based on preliminary learning, we are unlikely to get the desired outcomes from the process.
Even though many ML models may already be quite well developed and in use in the market, every company’s data can look different, and therefore the outcomes can change.
It’s important to ensure that you have a trained team of ML experts working with your model throughout the training process to ensure that all preliminary evaluations meet the expectations and that the results gained from the training process are moving toward the desired objectives.