Machine Learning Pipelines - The most important things summarized

Charlotte Goetz

Machine Learning has seen an impressive rise in recent years and has become a crucial tool in many industries. A critical component in the development of powerful machine learning models are so-called pipelines. These pipelines make it possible to define and automate complex workflows to prepare data, train models, and generate predictions. In this article, we explain the basics of machine learning pipelines and summarize the most important aspects.

This article was written in German, automatically translated into other languages and editorially reviewed. We welcome feedback at the end of the article.

What are machine learning pipelines?

Machine Learning Pipelines are a methodical approach to automate and structure the Machine Learning process. They enable the efficient connection and sequencing of different tasks to ensure a smooth execution of machine learning tasks.

Using machine learning pipelines automates complex and repetitive steps of model training and predictions. This facilitates the handling of large Data sets, as the pipelines facilitate the flow of data, the preparation and the Extraction optimize relevant information.

The pipelines enable a systematic and reproducible execution of machine learning tasks by combining the processing steps in a logical order. This creates a clear structure that simplifies model training and model selection. In addition, machine learning pipelines provide the ability to compare different models and algorithms and identify the best options for a given problem. They enable fast and effective evaluation of models to assess their performance and accuracy.

Why do you need ML pipelines?

ML pipelines are an essential tool in the world of machine learning. They provide a structured and efficient way to develop, train and deploy complex ML models. We have compiled the most important reasons why ML pipelines are indispensable for machine learning:

Data Management: ML pipelines help in the management of data. They enable the extraction, transformation and loading (ETL) of data from various sources. This process cleans and structures the data to prepare it for training models.

Model Training: Pipelines provide a systematic method for training models. They allow the selection and testing of different algorithms and hyperparameters. By automating the training process, multiple models can be developed and compared in parallel.

Feature Engineering: ML pipelines assist in extracting and selecting relevant features from data. They provide tools for transforming and scaling features to improve model performance.

Model validation: Pipelines enable evaluation of model performance through validation techniques such as cross-validation and metrics such as accuracy, precision, and recall. This allows the robustness and reliability of the models to be verified.

Scaling and deployment: ML Pipelines enable seamless scaling of models to large data sets and their efficient deployment in production environments. They automate the process of model versioning, updating, and monitoring.

In summary, ML pipelines are essential to manage the entire lifecycle of machine learning projects. They provide structure, efficiency and reusability, leading to faster development cycles, better models and improved data processing leads.

How do ML pipelines work? 

An ML pipeline is a framework that allows the various steps of an ML workflow to be seamlessly connected and instrumented. Similar to a factory where different machines and workstations work together in a specific order to produce a product, ML pipelines enable the seamless integration and execution of data processing and modeling steps.

The way ML pipelines work is based on the idea of sequencing and chaining operations. Each step in the pipeline takes input data, performs a specific operation, and passes the results to the next step. In this way, data can flow through various processing and transformation stages before being fed into a model.

What are the key steps in ML pipelines?

  1. Data preparation

    In the machine learning project, relevant data is first collected. These come from various sources such as CSV files, databases or APIs. Python libraries like Pandas, NumPy and Requests support data retrieval.

    This is followed by data cleaning, where errors, missing values and outliers are identified and corrected. Pandas and Scikit-learn provide functions for data cleansing and manipulation.

  2. Feature Engineering

    Data cleansing is followed by feature extraction, in which relevant features are extracted from the existing data. Python libraries such as Scikit-learn offer functions such as Principal Component Analysis (PCA) or Feature Scaling for feature extraction.

    Feature selection aims to identify the most important features and remove irrelevant or redundant features. For this purpose, Python libraries such as Scikit-learn, Recursive Feature Elimination (RFE) or SelectKBest are available that enable automatic feature selection.

  3. Model development and training

    Model selection is critical to the accuracy and performance of the machine learning system. Python offers libraries such as Scikit-learn, TensorFlow, and Keras with a wide range of models and algorithms for different applications.

    After model selection, the data is divided into training and test sets. The model is then trained on the training data and validated on the test data. Python libraries also provide features for model training and validation, including cross-validation and metrics such as accuracy, precision, and recall.

  4. Model evaluation and improvement

    After training and validation of the model, model evaluation is critical. Based on the model evaluation, improvements can be made to increase performance.

  5. Deployment and monitoring

    After model development and improvement, the model must be prepared for productive use. This includes saving the model and creating an API or user interface.

    After deployment, monitoring the model and its performance in the production environment is important. This includes monitoring metrics, detecting changes in data or behavior, and updating the model as needed.

From data preparation through feature engineering and model development to model evaluation and deployment, Python developers are well equipped to develop effective and scalable ML pipelines. Through the use of Python libraries, a wide range of tools are available to support each step of the pipeline and continuously improve model performance.

Open Source Components for MLOps Pipelines 

Open Source-components play a critical role in MLOps pipelines by providing flexibility and adaptability. We have identified five open source components that we believe add value:

  1. Apache Airflow: A framework for creating, scheduling and monitoring workflows.
  2. Kubeflow: A platform for orchestrating ML workflows on Kubernetes.
  3. TensorFlow Serving: A tool for deploying TensorFlow models as RESTful APIs.
  4. TFX (TensorFlow Extended): A framework for preprocessing, feature engineering, and model validation.
  5. MLflow: A framework for experimenting, logging and tracking models.

These open source components enable MLOps teams to create more efficient workflows that seamlessly integrate and automate ML model development, training, and deployment. The diverse options and active developer community make open source a valuable resource for MLOps pipelines.


Overall, the use of machine learning pipelines offers many advantages when it comes to training models and applying them in a productive environment. Pipelines allow you to efficiently preprocess data, train and validate models, and automatically store and export results. However, creating pipelines usually requires some preliminary work to link the different steps in a meaningful way and to adapt them to the specific requirements of a problem. The integration of new data or the use of other models may also require adjustments to the pipeline.

Machine learning pipelines are particularly suitable for applications that require a high data basis and complex modeling procedures. They provide an automated approach to model development and enable faster iterations and model improvement.

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