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A Short Guide on How to Build an End-to-end Ml Pipeline in 2024

Nowadays, ML and AI are not just trends but a downright revolution, whose impact extends to almost every aspect of a business. Many business leaders are seeing productivity gains through ML. The technology is growing rapidly, which is why the global ML industry is predicted to have a CAGR of 38.8% between 2022 and 2029. However, successfully implementing ML models in production is a task that requires advanced ML pipelines.

In this article, we will discuss how to build comprehensive ML pipelines today.

End-to-end ML pipeline – a brief definition

An end-to-end ML pipeline is an automated system of procedures that define the workflow of the ML model to solve problems. ML pipeline automation enables efficient data processing, ML model integration, evaluation of model effectiveness, and rapid delivery of results. With the modularity and flexibility of ML pipelines, domain-specific teams can efficiently

  • Build, test, and deploy models
  • Effectively manage ml operations
  • Monitor applied data

Building end-to-end ML pipeline


Data ingestion is the first step in the ML process. Information from various sources is sent unaltered to a data repository. Data ingestion involves collecting information from various sources, such as:

  • CRM, ERP
  • Consumer applications
  • The internet
  • The iot

Each data set has its pipeline, enabling simultaneous analysis and processing. By splitting the data within a pipeline, execution time is reduced. Central repositories, such as a database or data lake, serve as the destination for the collected data. You can use NoSQL databases for storing data. They provide expandable storage space for massive amounts of organized or unorganized data.


Data processing is a step that focuses on converting raw data into a format useful for ML models. In the distributed pipeline, it checks the data for structure, missing points, outliers, and other irregularities. If there are any problems, this pipeline corrects them. The feature engineering process is a key component, transforming raw data into variables used as inputs to models. The transformation pipeline includes operations, such as:

  • Aggregation
  • Normalization
  • Filling in missing values
  • Detecting outliers

The pipeline moves these variables into feature stores, repositories available to data scientists for model training.


The model training process includes a set of algorithms that can be used repeatedly. In this phase, dedicated pipelines use APIs to retrieve features from feature stores. This happens to load data into the modeling environment. Additional pipelines also generate diagnostic reports, which include:

  • Evaluating variable distributions
  • Correlations
  • Other statistical properties of the model

During this phase, the datasets are split into training, testing, and validation sets.


Next comes model evaluation. Data analysts test several models, comparing their accuracy and precision. The pipeline can run the models in parallel and store the results in a database. To select the best model, various metrics such as confusion matrices, mean squared errors, and learning curves are calculated. The main goal is to find a model that effectively solves the business problem and generalizes well to the new data. It’s also about ensuring that the model minimizes error by properly balancing bias and variance.


In this phase, ML engineers select the best model to implement in the production environment. Deployment pipelines operate in real-time, handling low latency for timely service delivery. They are responsible for retrieving user data when interacting with the app and converting it into predefined functions. All so that the ML model can use them for forecasting and then for sending the results back to the user’s application. The pipeline also stores key user activity data. This allows data scientists to evaluate the accuracy and usefulness of predictions. After selecting the best model, the pipeline deploys it for a smooth transition between old and new models.  However, for a comprehensive approach and seamless model deployment, seeking advice from an MLOps consulting service can be highly beneficial.


In the final phase, pipelines track the model’s performance by comparing its predictions with actual results. Monitoring also captures possible changes in the features used by the model, called data drift, which can signal significant changes in user behavior. In addition, those pipelines monitor changes in concepts. Monitoring pipelines should consistently track changes, whether in data, concept, or model performance. They should generate alerts and enable teams to take proactive steps to improve the model.

ML pipeline tools

ML pipelines use a variety of tools, libraries, and frameworks. For companies with limited resources, it can be difficult to hire a separate data analytics team to create ML pipelines. Therefore, for such companies, ML pipeline tools become crucial. They help develop, maintain, and track data processing flows. This impacts company efficiency through better data utilization and increased productivity.


ML and AI are revolutionizing business. However, effective use of ML requires building advanced pipelines. This process includes:

  • Data ingestion
  • Data processing
  • Model training
  • Model evaluation 
  • Model deployment
  • Model performance monitoring

The article also highlights the importance of ML pipeline tools, especially for companies with limited resources. 

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