A Brief Manual for Constructing a Complete ML Pipeline in 2024

A Brief Manual for Constructing a Complete ML Pipeline in 2024

Machine Learning (ML) and Artificial Intelligence (AI) have emerged as more than just passing trends in the business world. They represent a significant revolution that is impacting almost every aspect of business operations. A lot of business leaders have reported improvements in productivity as a result of implementing ML. Given the rapid growth of this technology, it’s not surprising that the global ML industry is projected to grow at a compound annual growth rate (CAGR) of 38.8% between 2022 and 2029. However, successfully incorporating ML models into production isn’t a straightforward task – it necessitates the development of advanced ML pipelines.

In this piece, we’re going to delve into the process of constructing robust ML pipelines in the current times.

Understanding what an end-to-end ML pipeline is

An end-to-end ML pipeline is essentially an automated system of processes that outlines the workflow of the ML model to solve problems. Automating ML pipelines facilitates efficient data processing, seamless integration of ML models, evaluation of model performance, and quick delivery of results. Due to the modularity and adaptability of ML pipelines, specialized teams can effectively:

  • Develop, assess, and launch models
  • Manage ML operations effectively
  • Monitor applied data

Creating an end-to-end ML pipeline


The first step in the ML workflow is data ingestion. This entails sending information from various sources in its original form to a data repository. Data ingestion involves gathering data from a wide range of sources, including:

  • CRM and ERP systems
  • Consumer applications
  • Internet sources
  • Internet of Things (IoT) devices

Each data set has its own pipeline, which enables simultaneous analysis and processing. By dividing the data within a pipeline, the time taken for execution is reduced. The collected data is sent to a central repository, like a database or a data lake. NoSQL databases, which provide scalable storage space for large amounts of structured or unstructured data, can be used for storing the data.


Data processing is the stage where raw data is transformed into a format that can be utilized by ML models. In the distributed pipeline, the data is checked for structure, missing points, outliers, and other anomalies. Any issues that are identified are corrected by this pipeline. Feature engineering, a process that converts raw data into variables that can be used as inputs to models, is a critical component of this phase. The transformation pipeline carries out operations such as:

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

These variables are then moved by the pipeline to feature stores. These are repositories that data scientists can access for model training.


The model training phase includes a set of algorithms that can be reused. In this stage, dedicated pipelines use APIs to retrieve features from the feature stores. This is done 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 stage, the datasets are divided into training, testing, and validation sets.


Model evaluation is the next phase. Here, data analysts test multiple models and compare their accuracy and precision. The pipeline can run the models in parallel and store the results in a database. Various metrics, like confusion matrices, mean squared errors, and learning curves, are calculated to select the best model. The primary objective is to identify a model that effectively addresses the business problem and generalizes well to new data. It’s also about ensuring the model minimizes error by properly balancing bias and variance.


In this stage, ML engineers choose the best model to implement in the production environment. Deployment pipelines operate in real-time, ensuring low latency for prompt service delivery. They are tasked with retrieving user data when the user interacts with the app and converting it into predefined functions. The ML model can then use these for forecasting and subsequently sending the results back to the user’s application. The pipeline also stores crucial user activity data. This enables data scientists to assess the accuracy and usefulness of predictions. After selecting the best model, the pipeline deploys it, ensuring a smooth transition between the old and new models. For a comprehensive approach and seamless model deployment, consulting with an MLOps service can be highly beneficial.


In the final phase, pipelines track the performance of the model by comparing its predictions with the actual results. Monitoring also captures potential changes in the features used by the model, a phenomenon known as data drift, which can indicate significant changes in user behavior. In addition, the 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.

Tools for ML pipelines

Diverse tools, libraries, and frameworks are used in ML pipelines. For companies with limited resources, hiring a separate data analytics team to build ML pipelines can be challenging. Therefore, for such companies, ML pipeline tools become crucial. These tools aid in the development, maintenance, and tracking of data processing flows, thereby improving company efficiency through better data utilization and increased productivity.

Wrapping up

ML and AI are bringing about a radical change in the way businesses operate. However, to effectively harness the power of ML, it’s essential to build advanced pipelines. This process encompasses:

  • Data ingestion
  • Data processing
  • Model training
  • Model evaluation
  • Model deployment
  • Monitoring model performance

This article also underlines the significance of ML pipeline tools, particularly for companies with limited resources.

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