A number of professions have emerged along with Artificial Intelligence in recent years, including Data Scientists and AI Engineers. The path to success appears to involve knowing and applying AI. However, those just starting out may find this path a bit discouraging. I’ve witnessed several common mistakes in my work as a Data Scientist and AI researcher, including my own, that made life hard for those just starting out. Here’s how to avoid these mistakes when building artificial intelligence models if you don’t want to waste time and motivation.
Do not Roughly Explore your Data
Be realistic about what you can expect from your model. In addition to being well-built, targeted, and often needing your help, the model also requires your involvement. Data analysis is the first step in doing that. There are many people who skip the exploration stage altogether and jump directly to creating the model. Those guys should start over from scratch if you are one of them. The following tip applies to you if you already know the importance of exploring data. There is no harm in exploring.
Here you will discover the correlation between your variables, discover how the data is distributed, and anticipate how the data will distribute in the future. Detect and treat outliers and anomalies, as well as remove all garbage that will hinder the next steps so that you will better understand the patterns within your data. Having all this knowledge, you can now create new features, which will be greatly appreciated by any AI model.
Analyze the theoretical basis for the models
It’s not necessary to have a Ph.D. to make AI models, don’t get me wrong. If you do not even know why an algorithm was created, you cannot apply it. AI / deep learning follows the same principle. By knowing if the model you intend to use matches your data, you may be able to save a lot of time and possibly achieve better results. In order to use an AutoML, the search parameters in the models must be understood. Let’s read a bit first, shall we?
Theory is your only Objective
In addition to studying how models work, it is crucial to mix theory with practice as well. A single individual cannot possibly study all the aspects of Artificial Intelligence, a small subfield of the field of Artificial Intelligence. I must admit some things are very complicated. You’ll soon get frustrated if you focus too much on theory and cannot apply it. Combining theory with practice will help you improve your coding skills as well as your understanding of artificial intelligence.
Knowledge of a Domain: Estimating its Value
It is now possible for you to build powerful applications based on AI since you have a good understanding of it. No matter what the problem is, you can solve it. Nobody ever said that. There will be particularities in each problem, and this will be extremely valuable for the model’s proper operation. You should not underestimate the value of people with years of experience in a particular field. They can help you better understand your data and even give you tips about how to do so. To make your model even better, read articles about that specific problem whenever possible.
Inability to Structure and Organise Experiments
I don’t know how to answer that. In the midst of his hundreds of experiments, every Data Scientist has once gotten lost. It has happened to me more than once. Organize yourself as much as you can. Developing a methodology for building experiments and saving your results so you can replicate everything you’ve done in the future is essential.
Jupyter Notebooks are frequently used by data scientists without even bothering to rename them, resulting in many untitled notebooks. Additionally, the notebook itself creates a lot of garbage within the code and executes it in different orders, as well as caches things you don’t even have anymore. You cannot keep all the experiments you have done a clean way after doing hundreds or even thousands of them (yes, this is common). As a result, you will not only have difficulty identifying what changes are improving or making things worse in your model, but you will also be unlikely to be able to replicate it. I know how frustrating it can be!
Having considered this problem and knowing that all data scientists experience it at some point, Amalgam developed Aurum to keep track of all changes in code and data and to make it easier to reproduce experiments and compare metrics across experiments, among others. If you want to avoid another headache, I suggest you take a look at this.
Training and Testing Require Different Transformations
The same mistake was made by more than a few students or colleagues while I taught AI. The training data must be treated exactly the same way as the test data, and the data treatment pipeline needs to be completed with the data once your model is in production. In this case, if your model is trained with one data distribution, some processing, and/or cleaning, but that does not apply to the data that will later be predicted, the model is predicting incorrectly. The data is being processed in formats for which it wasn’t trained, so the results will certainly not be as expected.
The model should also be saved if you are using any transformation algorithms, such as StandardScaler. Otherwise, your future data will be scaled differently, which is not desirable.
Validation Sets are not Good
Building a good validation set is the last but not least step in working with artificial intelligence (in fact, I consider it to be the most important step). Otherwise, all your experiments will be wasted. Regardless of the metric you choose, your model won’t be representative if you can’t validate it well.
In order to validate our model, we usually split a small percentage of our data. As much as possible, we will evaluate the generalization of our model within the possibilities of our data by making sure this set has a similar distribution of variables to the complete data set. This is referred to as a stratified split and ensures that your model learns when there are unbalanced classes in your datasets.
It is recommended to perform cross-validation whenever possible, which ensures that training and test sets do not overlap, as well as that k test sets do not overlap, which prevents biased evaluations. Don’t let your training data leak into your test data. In addition to appearing to perform well, you will not be able to determine whether your model is over-fitting if there is a leak in your data. As the information leaked for the test, you didn’t realize that your model had become addicted to the training data. Finally, the model is not generalized and in a real-life situation, it will certainly perform poorly.
The process of building an AI model can be quite challenging and involve some tricky aspects. Other problems will certainly arise, but just stay focused on avoiding these mistakes, and you’ll have more time for other challenges without losing motivation.
