Building a Robust Machine Learning Pipeline: Best Practices and Common Pitfalls

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Building a Robust Machine Learning Pipeline: Best Practices and Common Pitfalls
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In real life, the machine learning model is not a standalone object that only produces a prediction. It is part of an extended system that can only provide values if we manage it together. We need the machine learning (ML) pipeline to operate the model and deliver value.

Building an ML pipeline would require us to understand the end-to-end process of the machine learning lifecycle. This basic lifecycle includes data collection, preprocessing, model training, validation, deployment, and monitoring. In addition to these processes, the pipeline should provide an automated workflow that works continuously in our favor.

An ML pipeline requires extensive planning to remain robust at all times. The key to maintaining this robustness is structuring the pipeline well and keeping the process reliable in each stage, even when the environment changes.

However, there are still a lot of pitfalls we need to avoid while building a robust ML pipeline.

In this article, we will explore several pitfalls you might encounter and the best practices for improving your ML pipeline. We will not discuss the technical implementation much, as I assume the reader is already familiar.

Let’s get into it.

Common Pitfalls to Avoid

Let’s start by looking at the common pitfalls that often occur when building ML pipelines. I want to explore various problems that I have encountered in my work so you can avoid them.

1. Ignoring Data Quality Issues

Sometimes, we are fortunate enough to collect and use data from a data warehouse or source that we are not needing to validate on our own.

Remember that machine learning model and prediction quality is equal to the quality of the data we put in. There is a saying you’ve certainly heard: “Garbage in, garbage out.” If we put low-quality data into the model, the results will also be low-quality.

That’s why we need to ensure the data we have is suitable for the business problem we are trying to solve. We need the data to have a clear definition, must ensure that the data source is appropriate, and require that the data is cleaned meticulously and prepared for the training process. Aligning our process with the business and understanding the relevant preprocessing techniques are absolutely necessary.

2. Overcomplicating the Model

You are likely familiar with Occam’s Razor, the idea that the simplest solution usually works the best. This notion also applies to the model we use to solve our business problem.

Many believe that the more complex the model, the better the performance. However, this is not always true. Sometimes, using a complex model such as deep learning is even overkill when a linear model such as logistic regression works well.

An overcomplicated model could lead to higher resource consumption, which could outweigh the value of the model it should provide.

The best advice is to start simple and gauge model performance. If a simple model is sufficient, we don’t need to push for a more complex one. Only progress to a more complex approach if necessary.

3. Inadequate Monitoring of Production

We want our model to continue providing value to the business, but it would be impossible if we used the same model and never updated it. It would become even worse if the model in question had never been monitored and remained unchanged.

The problem situation may be constantly changing, meaning the model input data does as well. The distribution could change with time, and these patterns could lead to different inferences. There could even be additional data to consider. If we do not monitor our model regarding these potential changes, the model degradation will go unnoticed, worsening overall model performance.

Use available tools to monitor the model’s performances and have notification processes in place for when degradation occurs.

4. Not Versioning Data and Models

A data science project is an ever-continuous, living organism, if we want it to provide value to the business. This means that the dataset and the model we use must be updated. However, updating doesn’t necessarily mean the latest version will always improve. That’s why we want to version our data and models to ensure we can always switch back to conditions that have already proven to work well.

Without proper versioning of the data and the model, it would be hard to reproduce the desired result and understand the changes’ impacts.

Versioning might not be part of our plan at our project’s outset, but at some point the machine learning pipeline would benefit from versioning. Try using Git and DVC to help with this transition.

Best Practices

We have learned some pitfalls to avoid when building a robust ML pipeline. Now let’s examine some best practices.

1. Using Appropriate Model Evaluation

When developing our ML pipeline, we must choose evaluation metrics that align with the business problem and will adequately help measure success. As model evaluation is essential, we must also understand each metric’s meaning.

With model evaluation, we must monitor the metrics we have selected regularly, in order to identify possible model drift. By constantly evaluating the model on new data, we should set up the retraining trigger necessary for updating the model.

2. Deployment and Monitoring with MLOps

ML pipeline would benefit from CI/CD implementation in automating model deployment and monitoring. This is where the concept of MLOps comes in to help develop a robust ML pipeline.

MLOps is a series of practices and tools to automate the deployment, monitoring, and management of machine learning models. Using MLOps concepts, our ML pipeline can be maintained efficiently and reliably.

You can use many open-source and closed-source approaches to implement MLOps in the ML pipeline. Find ones that you are comfortable with, but don’t overcomplicate the system early on by including so many such tools that it would lead to immediate technical debt.

3. Prepare Documentation

One of the problems with data science projects is not documenting them enough to understand the whole project. Documentation is important for reproducibility and accessibility for our existing colleagues, new hires, and future self.

As humans, we can’t be expected to remember everything we have ever done, including every piece of code we have written, or why we wrote it. This is where complete documentation can help to recall any decision and technical implementation we decide to use.

Try to keep the documentation in a structure you understand and is easy to read, as sometimes the technical writing itself can become very messy and contribute to further problems. It also helps the next reader to understand the project when we need to hand over them.

Conclusion

Having a robust machine learning pipeline would help the model to provide continuous value to the business. However, there are some pitfalls we need to avoid when building them: