20 Machine Learning Best Practices for a Robust, Scalable Pipeline

Imagine you've built a powerful machine learning model that can solve a complex problem in your organization. But once you deploy the model into production, it doesn’t deliver the expected results. Instead, it fails, or worse, it produces inaccurate predictions that negatively impact your business operations. This scenario is, unfortunately, common. But organizations can avoid this messy outcome by following machine learning best practices. This article will explore the best practices for building a scalable, efficient, and reliable machine learning pipeline that minimizes failures, accelerates deployment, and delivers high-performing models in production. Additionally, understanding AI Inference vs Training is crucial to optimizing model performance and ensuring smooth deployment.

AI inference APIs are valuable tools to help organizations achieve their objectives and implement best practices for machine learning. Inference’s APIs streamline operations so that machine learning models produce accurate predictions for real-world applications.

What are the Common Pitfalls to Avoid When Building ML Pipelines?

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.

Core ML Pipeline Stages

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.

Robust Pipeline Design

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

Ignoring Data Quality Issues

In machine learning, data serves as the foundation for building accurate models. So, it should come as no surprise that the quality of this data directly impacts your model’s performance. Sometimes, we are fortunate enough to collect and use data from a data warehouse or source that we do not need to validate.

Data Quality Pitfalls

Still, it’s easy to overlook the quality of this data. The problem arises when we ignore the data quality and assume it is good enough to use immediately. This oversight can lead to disastrous results. Machine learning model and prediction quality equals the data quality we put in. There’s a saying you’ve undoubtedly heard: “Garbage in, garbage out.”

Ensuring Data Suitability

If we put low-quality data into the model, the results will also be low-quality. That’s why we must ensure our data suits the business problem we are trying to solve. We need the data to have a clear definition, ensure that the data source is appropriate, and ensure the data is cleaned meticulously and prepared for the training process.

Aligning our process with the business and understanding the relevant preprocessing techniques are necessary.

Overcomplicating the Model

You’re 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. Nevertheless, 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.

Balancing Complexity and Cost

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 complicated approach if necessary.

Inadequate Monitoring of Production

We want our model to continue providing value to the business, but that would be impossible if we used the same model and never updated it. It would worsen if the model in question had never been monitored and remained unchanged. The problem situation may constantly change, meaning the model input data also does.

Model Drift and Degradation or The Need for Continuous Monitoring

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.

Not Versioning Data and Models

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

Maintaining Model Relevance

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 initially be part of our project’s plan, but the machine learning pipeline would eventually benefit from it. Try using Git and DVC to help with this transition.

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20 Machine Learning Best Practices for a Robust Pipeline

In this section, we consider the business aspects of machine learning applications. This step is arguably the most important one. At the beginning of every project, we must define the business problem we are trying to solve. This will drive everything from the features of your application to the infrastructure and steps when it comes to gathering the data.

Here are some of the things you should pay special attention to during this process:

1. Start with a Business Problem Statement and Objective

As mentioned, making a business problem statement is crucial when building machine learning applications. Nevertheless, many people de-prioritize and overlook it since it is not techy and exciting. The advice is to spend some time on your problem, think about it, and consider what you are trying to achieve.

Profitability's Precision

Define how the problem is affecting the profitability of your company. Don’t just look at it from the perspective of “I want more clicks on my website“ or “I want to earn more money“. A well-defined problem is “What helps me sell more eBooks?“ Based on this, you should be able to define the objective.

The objective is a metric that you are trying to optimize. Establishing the right success metric is vital because it will give you a feel of progress. Also, the objective might change over time as you learn more about your data.

2. Gather Historical Data From Existing Systems

Sometimes, the requirements are unclear, so you cannot develop the proper objective immediately. This is often the case when working with legacy systems and introducing machine learning into them. Before you go to the nuances of what your application will do and which role machine learning will play, gather as much as possible from the current system.

This way, historical data can help you with the task at hand. It can also indicate where optimization is necessary and which actions will yield the best results.

3. Use Simple Metric for First Objective

Successful machine learning projects are incremental processes. You must be ready to iterate through several solutions to reach the final goal. That is why it is essential to start small. Your first objective should be a simple, observable, and attributable metric. For example, user behavior is the most straightforward feature to observe.

Things like “Was recommended item marked as spam?“ You should avoid modeling indirect effects, at least in the beginning. Indirect effects can give your business enormous values later, but they use complicated metrics.

Infrastructure Best Practices

Infrastructure has multiple roles in machine learning applications. One primary task is defining how we gather, process, and receive new data. After that, we must decide how to train and version our models.

Deploying the model in production is a topic we must consider. Infrastructure plays a crucial role in all these tasks. Chances are that you will probably spend more time working on the infrastructure of your system than on the machine learning model itself:

Here are some tips and tricks to consider when building it:

4. Feature Engineering

Feature engineering is another essential technique for improving model performance and speeding up data transformation. It involves infusing new features from existing features into your model. It can help us identify robust features and remove correlated or redundant ones.

Nevertheless, it requires domain expertise and may not be feasible if our initial baseline includes diverse features. Let's understand it from an example. Consider a dataset containing a house's length, width, and price. Instead of using these individual features, we can introduce a new feature called “area” and measure only its impact on the house’s price. This process is known as Feature Creation.

Feature Transformation and Extraction

Similarly, Feature Transformation and Feature Extraction can prove valuable depending on our project domain. Feature Transformation involves applying the transformation function to a feature for better visualization, while in Feature Extraction, we compress the amount of data by only extracting the relevant features.

The Significance of Feature Scaling

Although feature scaling is also part of Feature Engineering, I have discussed it separately to focus on its importance. Feature Scaling is the method used to normalize the range of independent variables and features. Why is this step so important? Most algorithms, such as linear regressions, logistic regression, and neural networks, use gradient descent as an optimization technique.

Gradient Descent and Data Ranges

Gradient descent heavily depends upon the range of features to determine the step size towards the minima, but most of our data vary drastically in terms of ranges. This compels us to normalize or standardize our data before feeding it into the model. The two most essential techniques in this regard are:

Normalization

Normalization is the technique for bounding your data, typically between ranges [0,1], but you can also define your range [a,b], where a and b are real numbers.

Standardization

Standardization transforms your data into a mean of 0 and a variance of 1. We first calculate the standard deviation and mean of the feature and then calculate the new value using this formula:

Contextual Scaling

There has been a lot of debate about which one is better. Some findings showed that standardization was more helpful for a Gaussian distribution as it was not affected by the presence of outliers and vice versa. But, it depends upon the type of problem that you are working on. Hence, testing both and comparing performance is highly recommended to determine what works best for you.

5. Infrastructure is Testable Without Model

Complete infrastructure should be independent of the machine learning model. In essence, you should strive to create an end-to-end solution where each aspect of the system is self-sufficient. The machine learning model should be encapsulated so the rest of the system is not depending on it.

Modular Design for Scalable Machine Learning Systems

This way, you can easily manipulate and restructure the rest of the system if necessary. By isolating parts of the system that gather and pre-process the data, train model, test model, serve model, and so on, you will be able to mock and replace parts of the system more efficiently. It is like practicing the Single Responsibility Principle on a higher level of abstraction.

6. Deploy the Model Only After it Passes Sanity Checks

Tests are an essential barrier that separates you from the system's problems. Do tests and sanity checks before deploying your model to provide the best experience to your machine learning application's users. This can be automated, too. For example, you train your model and perform tests on the test dataset.

You can check if the metrics you have chosen for your model are providing good results. Standard metrics like accuracy, F1 score, and recall can be used. If the model provides satisfying results, it will be deployed to production.

7. On-Premise or Cloud

Hardware or cloud? Bare metal or someone else's servers? This is an age-old question, the benefit of choosing a cloud is that it saves time, is easier to scale, and includes a low financial barrier to entry. You also have the support of the provider. Talking about providers, there are many options out there, with big players like:

• Microsoft Azure
• AWS
• GCP

On-premise hardware is a one-time investment, and you can run as many experiments as you like without affecting the costs. Many pre-built deep learning servers, such as Nvidia workstations and Lambda Labs, are available today.

8. Separate Services for Model Training and Model Serving

This is a conclusion that you can make from points 4 and 5. Nevertheless, it is essential, so it is good to mention it separately. In general, you should always strive to separate the training model component from the serving model component. This will give you the ability to test your infrastructure and model. Apart from that, you will have greater control of your model in production.

9. Use Containers and Kubernetes in Deployment

Microservices architecture can help you achieve the previous points. You should be able to encapsulate separate system parts using technology like Docker and Kubernetes. This way, you can make incremental improvements in each part and replace each component if necessary. Scaling with Kubernetes is also a painless process.

Data Best Practices

These “Software 2.0” solutions would not be possible without data. Data can come in many shapes and forms, and we often need to work hard to distill information from it. In this chapter, we cover some of the best practices for data gathering and pre-processing.

10. Data Quantity

You need a lot of data to make good predictions or detect patterns. That is why it is essential to set up the proper component in your system to gather data for you. If you have no data, it is good to invest in an existing dataset and then improve the model over time with the data gathered from your system.

Sometimes you can short-circuit the initial lack of data with transfer learning. For example, you can use YOLO if working on an object detection app.

11. Data Quality and Transformations

Real-world data is messy. Sometimes, it is incomplete and sparse; other times, it is noisy or inconsistent. To improve it, investing in data pre-processing and feature engineering is necessary. If you correctly encapsulate it, you can get the data from the data-gathering component and apply necessary transformations (like imputation, scaling, etc.) in the transformation component.

This component has two purposes. It prepares the training data and uses the same transformations on the new data samples that enter your system. In essence, it creates features that are extracted from the raw inputs.

12. Document Each Feature and Assign Owner

Machine Learning Systems can become large, and datasets can have many features. Features can sometimes be created from other features. It is good to assign each feature to one team member.

This team member will know why a specific transformation has been applied and what this feature represents. Another good approach is to create a document with a detailed description of each feature.

13. Plan to Launch and Iterate

Don’t be afraid to get into it and get better over time. Your features and models will change over time, so it is essential to keep this in mind. Also, the UI of your application might change, and you will now be able to get more data from user behavior. It is generally good to keep an open mind and be ready to start small and improve over iterations.

Model Best Practices

It may seem that even though machine learning applications revolve around the power of machine learning models, they are usually neatly tucked behind significant infrastructure components. This is true to a certain degree, but there is a good reason. Those other components are also necessary to utilize that power, but they are useless without a good machine learning model to put it all together.

14. Starting with an Interpretable Model

Keep the first model simple and get the infrastructure right. Don’t start with complicated neural network architectures right away. Maybe try to solve a problem with a simple Decision Tree first. There are multiple reasons for this. The first one is that building the complete system takes time.

Getting other components and data right in the beginning will allow you to extend your experiments later. Businesses will understand what is happening in the interpretable model, which can give you more confidence and trust to continue with fancier models.

15. Use Checkpoints

The best advice that one can give you when working with machine learning models is to use checkpoints. A checkpoint is an intermediate dump of a model’s internal state (parameters and hyperparameters). Using these machine learning frameworks can resume the training from this point on.

This will allow you to train the model incrementally and make a good trade-off between performance and training time. Also, this way, you are more resilient to hardware or cloud failures.

16. Performance Over Fancy Metric

Data scientists can often lose themselves in various metrics. This may lead to actions that improve various vanity metrics but lower the system's overall performance. Of course, this is a flawed approach, and the complete system's performance should always be the first priority.

Thus, look for another feature if some change improves log loss but degrades the system's performance. It is time to rethink the model's objective if this happens often.

17. Production Data to Training Data

The best way to improve your model over time is to use data during serving time for the next training iteration. This way, you will move your model to a real-world scenario and improve the accuracy of your predictions. The best way to do this is to automate it, meaning store every new sample from the serving model and then use it for training.

Code Best Practices

All that math, planning, and design need to be coded. It is the piece that holds it all together. It is essential to focus on your code to make a long-lasting solution. In this chapter, we share several tips and tricks that you should pay attention to regarding your project's code.

18. Write Clean Code

Learn how to write code correctly. Name your variables and functions like a grown-up, add comments, and pay attention to the structure. You can use object-oriented or functional programming and write many tests. Even if you work alone on the project, ensure you nail this because you will work in a team sooner or later.

Clean code helps all team members be in sync and on the same page. Never forget that the team is larger than the individual and that clean code is one tool for building a great team.

19. Write a Lot of Tests

Automate as many tests as possible. These are the guards of continuous progress. One can write several levels of tests when building one machine learning application. In general, write many unit tests to verify the functionalities of each system component. For this, you can use the Test Driven Development approach.

Rigorous Testing as a Gatekeeper for Model Quality

Integration tests are suitable for testing how components work with each other. Finally, system tests are there to test your solution end-to-end. Additional tests for the model are also part of this. Don’t save your model if it does not pass the sanity checks or put it in production. Performance tests can help you with this.

20. Documentation and Reproducibility

Have you ever revisited your project and come up with nothing? This happens to everyone, which is why documentation is key.

The Peril of Undocumented Projects

Let’s imagine we have already finished and delivered our customer churn prediction model. Nevertheless, some months later, we were asked to improve its effectiveness. You have already forgotten most of the project and have no documentation. That’s the worst scenario possible, as you must reintroduce yourself to your project… from scratch! This is why documentation is so necessary!

Best Practices for Effective Documentation

It fosters clear understanding, simplifies collaboration, and ensures reproducibility. Some documentation common good practices include:

• Clear and Concise Code: Use meaningful variable names, comments, and version control systems like Git.
• Detailed Reports: Document data collection procedures, feature engineering steps, model architectures, hyperparameter settings, and performance metrics.
• Model Serialization: Save trained models in a format that allows for reloading and making predictions on new data.

If proper documentation is performed, next time we go back to previous project we will have:

• Data: Source, feature engineering steps, details on each data point used.
• Model: Model architecture, hyperparameter settings.
• Training: Script with comments explaining each step, including data preparation.
• Evaluation: Metrics chosen (AUC-ROC, churn rate) and their interpretation.

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