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Machine Learning Pipelines: Everything You Need to Know

Machine Learning Pipelines

A machine learning pipeline is the backbone of many machine learning systems. They allow data scientists to take raw data and turn it into information used in real-world applications.

However, there is much more to them than just turning data into information! Machine learning pipelines are critical to the process of many businesses these days. Thus, understanding how building ML pipelines affects your future is critical.

This article will discuss what exactly they are, how they work, and some different types you might come across in your career as a machine learner.

When you’re ready to learn more about pipeline machine learning, keep reading and enjoy.

What Is a Machine Learning Pipeline?

Machine learning pipelines are a series of processing steps. They take raw data and turn it into information used in real-world applications. They allow data scientists to clean, transform, and model their data so they can get the most out of it.

The tools in the chain each have their specific use case and input types. Once all of these steps are completed, you end up with processed data ready for action! They may seem complicated at first glance, but they’re pretty simple when broken down into individual components.

Each component follows its own rules without caring too much about what happens next in the process. Thus, it’s easy to plug things in wherever you see fit using an appropriate tool for your situation.

This allows different people within an organization to work on various process components without stepping all over each other’s toes.

What Are Some Use Cases for Machine Learning Pipelines?

There are endless possibilities when it comes to how one can use data! Some common uses include:

  • Feature engineering
  • Feature selection
  • Time-series prediction
  • Image classification/categorization
  • Document classification/categorization
  • Spam detection
  • Sentiment analysis

It would take way too long to list them all out, but you get the idea — there is almost no limit to what types of information they can turn raw data into. This makes them invaluable in any industry that requires heavy analytic processing, including finance and text mining.

How Do Machine Learning Pipelines Work?

The basic idea behind a machine learning pipeline is to break down the task at hand into a series of smaller tasks that one can complete more easily.

This allows you to build a more modular system that is easier to debug. Each step in the pipeline performs a specific task.

One example is cleaning up your data, transforming it into a format suitable for modeling. There is also training for a particular machine learning model.

Furthermore, each step can be implemented using various tools that are best suited for the task at hand.

This allows you to choose the right tool for the job and prevents anyone from having too much control over the entire process. It also makes it easy to swap out individual steps if needed without affecting the rest of the pipeline.

What Types of Machine Learning Pipelines Exist?

There are a few different types of machine learning pipelines you will encounter in your data scientist career: feature extraction, feature embedding, and stacking. There are also some other ones that we will examine.

A feature extraction pipeline extracts features from the raw input data before experimenting with creating new datasets with only high-quality information inside. Feature engineering is an essential part of building successful models and more efficient ones!

On the other hand, feature embedding involves using some transformation technique that converts continuous or discrete variables into numerical vectors called “embeddings,” which can then be used for modeling tasks such as clustering and classification.

Stacking combines multiple pre-trained models to be used as a single unit. This is often done when the models have been trained on different data types, and you want to use them all together in one system.

Globalized ML Pipelines

Besides these three, there are some other examples of a machine learning pipeline. Let’s take a look at each in greater detail.

Pre-Processing Pipelines

The first type is called pre-processing. As its name suggests, this type of pipeline is responsible for all the pre-processing that needs to be done on the data before one can use it for machine learning.

This includes cleaning, formatting, and transforming the data into a suitable input format.

Data Augmentation Pipelines

The second type is called a data augmentation pipeline. This type is used to increase the size of your training dataset by artificially adding new data instances.

This is often done by applying transforms (such as translations, rotations, or scaling) to existing images or examples to create more varied datasets.

Model Training Pipelines

The third type is called a model training pipeline. As you might expect, this pipeline is responsible for training machine learning models. This is often done in a linear or sequential order in simple pipelines.

More complex models might also use parallel, and even ensemble learning approaches. This is to improve the overall accuracy of the system as well as to reduce training times.

Model Deployment Pipelines

The last type is called a model deployment pipeline. This one helps deploy your machine-learning models into production systems where end-users can use them interactively.

While there are many examples of deploying these types of models, we will focus on three common ones: web services (APIs), serverless prediction engines, and mobile app backends for smartphone applications such as voice assistants like Siri or Google Assistant.

Overall Machine Learning Pipeline Workflow

Although each component has its function, it’s essential to realize that one can combine these pipelines into a single machine learning pipeline. This type of workflow is often used when the goals and requirements for each component do not overlap with one another.




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