Machine Learning

What are machine learning pipelines, and why are they important?

May 23, 2022

Want to know what goes into a machine learning pipeline?

A machine learning pipeline is a step-by-step process for training and deploying AI models. It’s an important part of any AI project, but it can be difficult to understand. It’s important to understand the different steps involved in building a machine learning pipeline so you can use it effectively.

This post is going to show you what a machine learning pipeline is, and how you can use one in your business to optimize KPIs.

What is 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 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 machine learning pipeline. 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

The first reason is most important for organizations just starting their journey with machine learning. It can take a long time to get machine learning models up and running if you're doing it manually. Each task in the process, including data preparation, feature engineering, model training, and evaluation, can take days or even weeks.

With a machine learning pipeline, you can automate all of these steps. This means that you can get your models up and running much faster, which leads to a shorter time-to-value. In some cases, you may be able to get your first models up and running in just a few days or weeks.

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. 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. This is especially important if you have team members with different levels of expertise with machine learning tools and techniques.

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. The more data you have, the better your models will be.

Leverage the power of AI

Another reason to use a machine learning pipeline is that it 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

As mentioned earlier, one of the advantages of a machine learning pipeline is that it minimizes the chances of human error. This is 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. By using a machine learning pipeline, you can avoid these problems altogether.

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. Data preprocessing is a key part of any data pipeline, since raw data needs to be formatted before it can be used in a machine learning workflow.

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. This stage of the lifecycle can include data analytics tasks like visualization, as well as feature extraction.

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.

3. Model training

Once the data has been prepared, it can be used to train machine learning models. The goal of this step is to build models that are capable of making accurate predictions based on new data.

To do this, you will need to select appropriate algorithms and optimize their hyperparameters. This can be a time-consuming process, but it is crucial for building accurate machine learning models.

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.

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. This step is important because it ensures that the models are capable of making accurate predictions in the wild. 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, and retrain the model. After all, the ML workflow is an iterative process.

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.

Scaling is the process of increasing or decreasing the number of resources (e.g. CPU, memory) that are allocated to a machine learning model. This is important so that the model can be tuned to the needs of the application.

5. Optimization

The final step in a machine learning pipeline is often optimization. This process can be done in a number of ways, but one common method is to train the model on new or additional data.

There are a few reasons why this approach can be effective. First, it allows the model to learn from more data, which can improve the accuracy of the predictions. Second, it can help to avoid overfitting, which occurs when a model Memorizes the training data too closely and does not generalize well to new data.

One 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.

With Akkio, not only can you effortlessly train on new data, 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 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 reduction, attrition prediction, fraud detection, lead 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 how Akkio can help, try a free trial today.

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