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How to optimize your ML pipeline: Rules to consider

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Machine learning is a subset of artificial intelligence that focuses on data predictions based on well-trained algorithms, resulting in a fully-fledged machine learning model. In computer vision, those models go on to power major processes in our day-to-day lives, from something as “ordinary” as facial recognition on our phones to detecting anomalies and diseases in medical imaging. But before reaching that far, a fully-functioning and efficient model needs to be created via a string of core processes to reach the desired result. Any data scientist will know that the primary challenge is not creating a model but creating one that is standardized, scalable, and passes the test of time. That’s precisely where an end-to-end machine learning pipeline comes into play.

how to optimize machine learning pipeline

In this article, we’ll discuss the following key points:

  • What is a machine learning pipeline?
  • Benefits & challenges
  • Building machine learning pipelines
  • Rules for optimization
  • Key takeaways

What is a machine learning pipeline?

A machine learning pipeline otherwise referred to as an ML workflow, is a method for codifying and automating the workflow required to create a machine learning model. By taking several steps in a subsequent order, we can achieve a fully-functioning ML pipeline. The four main steps in an ML pipeline are data preparation, model training, model deployment, and monitoring. However, there are processes that exist in between those steps to make the pipeline whole, as you’ll see in the ML pipeline example below. Those four are considered the four pillars that make up the process, though. We’ll take a closer look at what role each step of an ML pipeline plays in order to guide you in setting up your own pipeline effectively.

If you’ve heard of DevOps and know its core principles, then it’ll be easier to grasp a similar methodology known as MLOps. The implementation of a machine learning pipeline promotes ML operations by automating and tracking all processes from the initial ingestion to launches and rollout and monitoring after deployment by providing additional control over the entire assembly line.

model life cycle
Model life cycle: Image source

With close examination, we can quickly realize that the ML pipeline is not a linear, straightforward process, but it resembles more of a cycle when fully put into place. One of the reasons is that data is always emerging, so you need to build a model blueprint that can easily incorporate new data.

Benefits & challenges

It’s important to weigh in the facts and pinpoint when it’s necessary for your project to invest time and resources into implementing an ML pipeline because, although it is greatly favored by specialists, it isn’t always a necessity depending on the project.

  • Standardize your model — With a standardized machine learning pipeline, you won’t need to worry about discrepancies in information structure. This facilitates the migration of data scientists across teams by enhancing the onboarding process. In other words, it shortens the time it takes to get started on a new project. The effort spent in integrating a machine learning pipeline might also result in a higher retention rate.
  • Scale models and focus on new ones — Data scientists will be relieved of the burden of maintaining current models thanks to automated machine learning workflows. We've seen far too many data scientists spend their days updating previously created models without the time to focus on moving on to the next best thing. Scale your annotation pipeline and tap into greater potential for expansion instead of dedicating all of your resources towards maintaining a single model.
  • Move out of the “one-time model” phase — New information is constantly emerging, and developing an ML model that can exploit this is a necessity more often than not. That is, adapting to the new data in order to keep the target accuracy. Unlike a one-time model, an integrated ML pipeline can process a continuous stream of raw data gathered over time. This allows teams to establish a continuous teaching process where the model learns from fresh data and provides up-to-date results for real-time automation on a large scale.


  • Associated costs — Businesses can come up against a financial standstill when developing a pipeline in their machine learning models since it requires significant resources to implement. The prices of creating and maintaining a machine learning model can cost around $60k for a five-year bare-bones approach or upwards of $95k for a five-year optimized and MLOps-integrated model. That includes a plethora of costs, including orchestration, infrastructure, data support, engineering, and more.

You read it correctly — there aren’t any other machine learning pipeline challenges that will limit your ML model. The presence of a machine learning pipeline is proven to bring more good than bad to a project.

Building machine learning pipelines

In order to build an end-to-end machine learning pipeline, it’s important to consider the necessary steps, their order, and significance in the pipeline to ensure each individual component is providing the full value to the model. Those steps consist of:

building machine learning pipelines
  • Data preparation — Preparing the data is a vital first step in the ML pipeline since the clarity and quality of the data dictates how accurate the output results will be. Whether you collect data from single or multiple sources, consider only data that is relevant, labeled properly, and clean.
  • Data preprocessing — Once the data is collected, it can be put to use but not before it is processed to ensure the training process will be executed swiftly and without discrepancies. This step entails converting your collected and labeled data into a format that is “readable” by your algorithm, which is different from what we covered in a standardized machine learning pipeline.
  • Model training — Now, you can move onto another vital step that can make or break your model; that is training the model based on the data you have collected. Test the model by inputting your training data and analyzing the output until you receive the desired accuracy.
  • Model deployment — Once the desired accuracy is achieved, the model is ready for deployment in a production environment. From this point onwards, an iterative process is initiated where the model may be further retrained to improve accuracy in the future.
  • Model monitoring — If you have MLOps and a fully-fledged pipeline in place, monitoring should be a simple yet nonetheless important step of the process. It’s always necessary to keep an eye out for errors in performance.

Rules for optimization

A machine learning model that is up and running is a good thing — but there is always room for improvement. At the pace that innovation is moving now, your model needs to be optimized in order to be long-lasting and flexible, all without compromising on quality and accuracy. Let’s take a look at some pro tips on how to optimize your machine learning pipeline.

#1: Prioritize ML orchestration

All of the tasks in the machine learning pipeline are intertwined with one another, where the functionality of one depends on the process executed before it. In order to affirm that all of the individual processes are functioning properly (which in its turn reflects how the entire pipeline is functioning), there is a need for orchestrating the processes. The best part about ML orchestration is that a plethora of ready-made tools are available in the market with free/affordable template options. It’s vital to consider looking into orchestration tools and software in order to optimize your machine learning pipeline.

#2: Hyperparameter tuning

When new data emerges, it’s vital that a machine learning model can assess it and update the results accordingly with the latest trends. However, too much variation in the data can skew the accuracy of the algorithm altogether. That can be stabilized by fine-tuning hyperparameters. The selection of the hyperparameters is not done by the model but are manually adjusted as a form of customization towards model accuracy. There are three common approaches to hyperparameter tuning: grid search, random search, and Bayesian optimization. It’s best to take a look at all of your options to determine the best fit for you.

#3: Automate labeling

Another one of the machine learning pipeline best practices to add to your checklist if you haven’t already is automating the process of asset labeling. Hand labeling is accurate and effective, but only up until the point that you begin to work with massive streams of data. Did you know that labeling data takes up much more compared to the time spent on the total ML workflow? Not only does that open up possibilities for human error, but the task of hand-labeling becomes a tedious process. For that reason, it’s best for teams to implement their choice of automated labeling workflow to streamline the process and only manually label necessary assets.

#4: Additional training data

“Data” is an important word for the world of machine learning — there is no running from it. Similarly, your model cannot only exist based on the initial training data it was created with. Information changes rapidly in the fast-paced world, and your model needs to keep up with it, which is why the model needs to be refreshed every now and then with new data points. For the most part, more data means increased accuracy, but you need to ensure that there isn’t overwhelming data variation, as mentioned in rule #2.

Key takeaways

A machine learning pipeline is a cyclical process, as we’ve established, which aims at standardizing, codifying, and automating vital machine learning processes that go on to complete a full cycle of an ML model. By establishing an ML pipeline, you can swiftly adapt your model with new data, decrease data scientists’ time spent on revisiting the model, and improve the accuracy of the main task of your model, whether it’s aimed at image segmentation or other forms of pattern recognition. With maximum benefits and minimal limitations or challenges in your path, many data specialists will argue that integrating an ML pipeline is a necessity down the road. Whether you’re developing your ML pipeline for scratch or finding ways to optimize it to reach new heights, feel free to use all of our tips and steps as a checklist to guide you along the way.

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