Machine learning (ML) projects can be complex and time-consuming, and it’s easy to make mistakes along the way. In this article, we’ll go over 10 common mistakes people make with ML projects and how to avoid them.
Not clearly defining the problem for ML Projects
Before starting any ML project (really any project for that matter), it’s important to define the problem you’re trying to solve. Without a clear understanding of what you’re trying to accomplish, it’s easy to get sidetracked and waste time and resources.
What is the purpose your Machine Learning project? What problem are you solving that can’t be solved without machine learning? How do you know if the machine learning project is actually providing the results you want? Do you know what is a good point to stop when things are either working or not working?
Listen to our podcast on the real-world mismatch: one of the key reasons for ML and AI project failure
Insufficient Data for ML Projects
ML models require a lot of data to train and validate. If you don’t have enough data, your model may not be able to learn the patterns you’re trying to find, and the results will be less accurate and potentially biased. Make sure you have enough data before starting your project.
The question of just how much data do you really need is frequently asked. The short answer is – it depends. It depends on the project, the algorithm you’re using, and how accurate you need your results to be. However, data is at the heart of the modern enterprise helping them better understand their customers, make better business decisions, improve business processes, track inventory, and many other critical functions. So harness the data you have to make sure you’re getting the most accurate results you can.
We have a lot to say on the AI Today podcast on the topic of data quantity and data understanding needed for AI projects.
Wrong Type or Quality of Data for ML Projects
Not all data is useful for machine learning projects. Data quality issues often sink AI projects. Make sure you’re using the right type of data for your problem. For example, if you’re trying to predict customer churn, you’ll want to use data about customer behavior, not data about website traffic. Machine learning projects do need a lot of data but you want the right kind of data.
Data quality issues also cause significant problems for machine learning projects. From data full of incorrect or missing values to inconsistent or inadequate data for systems, data with inconsistencies to required data not present, data quality presents significant challenges to making machine learning systems work. Without knowing in advance the quality or consistency of data for your machine learning projects, you’re setting yourself up for possible failure.
Improper Data Splitting
When training and validating your ML model, it’s important to split your data into training, validation, and test sets. Not splitting the data properly can lead to overfitting, where the model performs well on the training data but poorly on new data it’s never seen before. You don’t want to invest money and resources into your ML project only to find out that it doesn’t work as expected before you didn’t properly split your data for training and testing.
Check out our AI glossary entries on data splitting.
Incorrect Algorithm Selection
There are many different types of ML algorithms to choose from, and each one is suited to a different type of problem. Make sure you choose the right algorithm for your problem. There is no one size fits all when it comes to selecting the right algorithm for a particular machine learning task. For example, if you’re trying to classify images, a convolutional neural network (CNN) would be a better choice than a linear regression model. For predictive modeling, simpler approaches such as regression or decision trees could work, and for autonomous systems you might need to combine multiple algorithmic approaches.
Improper Hyperparameter Tuning
Even if you choose the best algorithm for a machine learning task, it’s important to tune the hyperparameters to get the best performance. This is usually done through a process called hyperparameter tuning, where different combinations of parameters are tried to find the best one. There are tools out there that can help you do this such as autoML tools. Experienced data scientists can also do this. Whatever your approach, make sure you have someone on your team who understands hyperparameter tuning.
Improper Model Evaluation
It’s important to evaluate the performance of your model using metrics like accuracy, precision, and recall. Not evaluating the model properly can lead to overfitting or underfitting, where the model doesn’t perform well on new data. As with anything, you need to test what you have created to make sure it’s working as expected. Don’t assume that your model will perform correctly on the first try and that continual evaluation is not necessary.
Model evaluation is one of the Five Steps for an AI Project, and what is often missing in machine learning projects.
Not Deploying the Model Properly
Once you’ve trained and validated your model, it’s important to deploy the model properly. This includes things like setting up a monitoring system to track the performance of the model in production, and updating the model as new data becomes available. It also means deploying the model where it can be used. For example, if you have a facial recognition model that is used to unlock a mobile phone, that model should live on the phone and not in the cloud. You can’t expect mobile or edge devices always be connected to the internet as people can be in remote locations where internet service isn’t available such as hiking in the woods.
Not interpreting the results correctly
ML models can be complex, and it’s easy to misinterpret the results. Make sure you understand the model’s output and how it relates to the problem you’re trying to solve. It’s important to also have the correct role and skill sets on your team so results can be properly interpreted.
Not updating the model
ML models are not static. You can’t just build them once and then never monitor them again. As we always say: It’s never a set it and forget it type thing. The models need to be regularly monitored and updated as needed when new data becomes available. Make sure you have a plan in place for updating the model to avoid model decay.
This is something we dive deep into in our AI Failure Series podcast on not understanding the model and data lifecycle.
Avoiding Common Mistakes and Being More Successful with Machine Learning Projects
By avoiding these common mistakes, you’ll be able to create ML models that perform well over the long haul and provide valuable insights. Remember, business requirements change. Technology capabilities change. Real-world data changes in new or unexpected ways. By making sure you always understand what problem it is you’re solving and then using the correct data, algorithm, and processes to test, deploy, and monitor and update the model as needed you’ll be in good shape for success. With these tips in mind, you’ll be well on your way to creating successful ML projects and avoiding common mistakes that can cost you serious time, money, resources, and project failure.
