Published on

January 5, 2024

Machine Learning

What Are machine Learning Pipelines, and Why Are They Important?

Want to know what goes into a machine learning pipeline? This post is going to show you what a machine learning pipeline is, and how you can use one.
Ekin Keserer
Co-Founder, Head of Design, Akkio
Machine Learning

A machine learning pipeline is a step-by-step workflow for developing and deploying machine learning models into production. These pipelines allow you to streamline the process of taking raw data, training ML models, evaluating performance and integrating predictions into business applications.

Developing efficient machine learning pipelines is key to successfully leveraging artificial intelligence. With the right pipeline, you can improve model accuracy, reduce errors, and accelerate the deployment of AI models.

This article will walk you through the key steps for creating effective machine learning pipelines. You’ll learn best practices for data preparation, model training, model evaluation, and deployment. Follow along to gain the knowledge needed to build ML pipelines that drive real business value.

What is a machine learning pipeline?

A generated AI image illustrating an idealized representation of a machine learning pipeline.
A generated AI image illustrating an idealized representation of a machine learning pipeline.

A machine learning pipeline is simply a set of steps that you follow while working on your project. This could include things like organizing your data, training models, and deploying them to make predictions. 

Pipelining is important because it helps you organize your machine learning workflows and makes your process faster. By linking different steps together, you can save time and effort. Plus, it can help you train your data more effectively. In short, a machine learning pipeline is a crucial tool for anyone working on a machine learning project.

For instance, suppose you want to use machine learning to predict and prevent customer churn. Your pipeline might look something like this:

  1. Organize your customer data. This could involve things like accessing customer data from Salesforce, cleaning it, and pre-processing it in a way that can be used by your machine learning models. 
  2. Train your models. This step involves using your data to train one or more machine learning models, which, in this case, could be used to predict customer churn. A lot of computing power is needed, typically provided by platforms like AWS and Azure.
  3. Evaluate your models. Once you've trained your models, it's important to evaluate their performance to see how well they are actually doing. This will help you fine-tune your models and make sure they are as effective as possible.
  4. Deploy your models. After you've evaluated and fine-tuned your models, you can deploy them in production so they can start making predictions (in this case, predictions about customer churn).

This is just one example of a typical machine learning pipeline used. The steps you take will vary depending on your project and goals. But in general, a machine learning pipeline can be a helpful tool for anyone working on a machine learning project.

Why should you use a machine learning pipeline?

If you're looking to get started with machine learning, or even if you're already using it for predictive modeling, you should consider using a machine learning pipeline. Here are 5 reasons why.

Decrease time-to-value

For organizations initiating their machine learning journey, efficiency is paramount. Manually setting up machine learning models is time-consuming, with processes like data preparation, feature engineering, model training, and evaluation spanning days or even weeks.

Leveraging a machine learning pipeline can dramatically streamline these tasks. This not only speeds up the model deployment but also significantly reduces the time-to-value. With tools like Akkio, you can have your models operational within mere minutes or hours.

Consistency across team members

Another reason to use a machine learning pipeline is that it helps ensure consistency across team members. Since the process is automated, everyone will be working with the same data in the same way regardless of their skill level or knowledge of the overall process.

This is important for two reasons: First, it ensures that everyone is working with the most up-to-date data. Second, it minimizes the chances of human error.

Leverage the power of big data

If you're not already leveraging the power of big data, you're missing out on valuable insights about your business and how it can grow. With a machine learning pipeline, you can easily tap into large data sets to train and evaluate your models.

This is important because machine learning models are only as good as the data they're trained on. Generally, the more quality data you have, the better your models' output will be.

Leverage the power of AI

Using a machine learning pipeline is what allows you to leverage the power of artificial intelligence without having to hire dedicated experts or spend months on research and development.

With a machine learning pipeline, you can easily take advantage of existing ML tools and techniques. This means that you can get started with AI without a lot of upfront investment.

Reduce the chances of human error

Reducing the chances of human error is one of the advantages of a machine learning pipeline because the process is automated and there are defined steps that everyone needs to follow. Human error can be costly and time-consuming to fix.

Steps involved in building a machine learning pipeline

Machine learning pipelines are a great way to automate repetitive tasks and improve the accuracy of your predictions. Here are the five steps involved in building a machine learning pipeline.

1. Data collection

Data is the fuel necessary for any machine learning algorithm. Without data, there can be no predictions made. The first step in building a machine learning pipeline is to collect data from various sources.

Crucially, this data must be indicative of the real-world system that the machine learning algorithm will be deployed in. For example, if you want to build a machine learning pipeline for website analytics, then your data collection process should focus on acquiring data about website visitors.

The data must also be high quality and free of duplicates or missing values. Once you have collected the data, it must be stored in a central location so that it can be accessed when needed.

2. Data preparation

The second step in building a machine learning pipeline is to prepare the data for modeling. This process involves cleaning up the data, transforming it into useful formats, and adding additional information that will help improve the accuracy of predictions. For example, if you are working with text data, then you will need to convert it into numerical data before it can be used for training models. You may also need to remove duplicate values or impute missing values. Once the data is ready, it must be split into training and testing sets. The training set is used to train the model, while the testing set is used to evaluate the performance of the model. These steps would traditionally be done by data science experts and data engineers using tools like Python.

Akkio automatically handles such data preparation tasks so that you can focus on building machine learning models. Akkio also features allows you to interact intuitively with your data using Chat Data Prep.

Showcasing the Chat Data Prep feature in Akkio

3. Model training

Once the data has been prepared, it can be used to train machine learning models. To do this, you will need to select appropriate algorithms and optimize their hyperparameters.

Akkio automatically tunes hyperparameters for hundreds of machine learning algorithms and selects the best model for your data. Using Neural Architecture Search, Akkio can find optimal neural network architectures for your data in minutes.

How a trained model looks like in Akkio

4. Testing and deployment

After the machine learning models have been trained, they need to be tested against real-world data to assess model performance. If the model is less accurate than it could be, you can revisit the data sources and data flows used to ensure high-quality input data, and retrain the model.

Once the models have been tested, they can be deployed into production where they will make predictions based on new incoming data. Akkio makes it easy to deploy machine learning models into production and keep them up-to-date as new data arrives.

Akkio's machine learning deployment platform handles all of the heavy lifting required to get machine learning models into production and keep them running smoothly, including scaling.

Showcasing deployment options in Akkio
Deployment Options in Akkio

5. Optimization

The final step in a machine learning pipeline is often optimization. One of the most effective ways is to train the model on additional data. This allows the model to learn from new data, which should improve the accuracy of the predictions. Second, it can help to avoid overfitting, which occurs when a machine learning model memorizes the training data too closely and does not generalize well to new data.

The only downside of this method is that it can take more time to train the model on new data. However, this is often a worthwhile trade-off in order to improve the accuracy of the predictions it outputs.

With Akkio, not only can you effortlessly train multiple models on new data, removing the downside, but you can also do so 100X faster than traditional methods, making it the perfect solution for optimizing machine learning models.

What’s the easiest way to create an ML pipeline and use ML in your business?

As you've now seen, there are several steps in an ML pipeline, including data processing, model selection, training, validation, and deployment. Akkio can help you through every single one of these steps - let's take a look at how. 

Akkio is a no-code platform that enables companies to leverage and scale the power of AI and machine learning using existing data from their applications and platforms like Google Analytics, Salesforce, and so on to make better decisions faster. No-code integrations make it possible to deploy ML models anywhere with no need for coding, while Akkio's API enables more custom deployments.

Most of the ML pipeline process is automated for you, so anyone on your team who has access to the right permissions can use ML - they don't need to be a data scientist. The easy-to-use interface makes it simple to get started, and you can be up and running in no time. 

There are a broad range of use cases, including churn reductionattrition predictionfraud detectionlead scoring, and more. Check out our tutorials page to get a better sense of the kinds of metrics you can optimize.


Creating a successful machine learning pipeline is essential for any organization that wants to stay competitive in today's data-driven world. Akkio makes it easy to use best practices in your ML pipeline so you can get the most out of your data.

Using high-quality data collected over time is crucial for training accurate machine learning models. Akkio automatically ingests data from any application, making it easy to get started with machine learning without needing to manually collect data.

Choosing the right model for the problem at hand is also important. Akkio provides built-in algorithms for common tasks like linear regression, decision trees, and deep learning, so you can get started without writing any code.

Testing frequently throughout the process is essential to catch any errors and assess the results of your predictions. Akkio lets you monitor every step of the machine learning process in real-time, so you can identify any issues and make changes as needed.

By following these tips, you can set your organization up for success with machine learning. To learn more about machine learning systems how Akkio can help, try a free trial today.

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