Set Up Your First Machine Learning Pipeline With This Beginner’s Guide

Building and running machine learning (ML) models is a rewarding yet time-consuming and complex process of data preparation, feature generation, model fitting, validation, and deployment stages. What's more, these models need to be updated frequently as trends in data shift. Otherwise, they become stale and make low-quality predictions. That's why an end-to-end ML pipeline is a must to automate workflows for scalability and efficiency. This pipeline makes it easier to develop, test, and deploy fresh models consistently. In this article, you'll learn about ML pipeline and how to create one for your needs. What Is a Machine Learning Pipeline? An ML pipeline is a systematic automation of the multiple stages of an ML workflow, with each step in the pipeline independent from the other but still aimed toward a collective outcome. This desired outcome is to go from raw data to high-quality predictions through multiple sequential steps, starting from data extraction and preprocessing to model training and deployment. A machine learning pipeline follows a modular approach. That is, each stage is an independent unit that functions separately. Yet, all units come together to get the final outcome. Instead of manually collecting and processing data, training a model, validating its quality, and finally deploying it, an ML pipeline automates these repetitive processes. By doing so, the pipeline makes the management and maintenance of models more efficient and less error-prone, which ultimately improves the accuracy and reliability of these models. ML pipelines help data scientists and artificial intelligence (AI) engineers to efficiently manage the complexity of the ML process by providing them with a scalable and durable solution to develop, produce, and update AI systems. It can be used for single or multiple models. A well-executed pipeline makes the implementation of ML workflow more flexible. It also allows you define the required features, model parameters, and monitored metrics to produce and update the most crucial component of the pipeline: the model. However, do not let the word “pipeline” trick you into thinking it's a one-way flow because it isn't. ML pipelines are cyclic to enable iteration. Note: An ML pipeline is different from a data pipeline. The goal of a data pipeline is to move data between systems while transforming them. However, an ML pipeline is focused on streamlining and speeding up complex ML processes for greater efficiency. A Step-by-Step Guide to Creating a Machine Learning Pipeline Irrespective of the use case, most ML pipelines follow the general ML workflow. Thus, there are some typical stages that are similar in most pipelines. Each of these stages builds upon the preceding stage. That is, the output of the prior stage becomes the input for the new stage until the final outcome is achieved. Below are the stages in a typical ML pipeline: 1. Data gathering or collection This is the first stage in an ML pipeline. Here, raw data will be collected and recorded from sources such as Application Programming Interfaces (APIs), surveys and questionnaires, online databases, institutional records, files from government agencies, and many more. The data source can be primary (first-hand research) or secondary (existing resources), depending on the specifics of the ML use case. You can use any of the powerful data collection libraries in Python (Request, Beautiful Soup, Scrapy, and Selenium) for this stage. Meanwhile, because this data is raw, unstructured, and messy, it's not yet ready for ML analysis, hence the next stage. 2. Data preprocessing In this stage, the data will be cleaned and organized in a usable format for efficient analysis and model training and testing. If this stage is skipped, the data will be unsuitable for the model. That is, the model won't produce any tangible result with the dataset. Some data preprocessing steps in ML include sorting out missing data, handling duplicates, reducing noisy data, and feature engineering (which I discuss in the next stage). Pandas, NumPy, Scikit-Learn, and Scipy are helpful Python libraries for data preprocessing. The ultimate goal of data preprocessing is to prepare the data for feature engineering. 3. Feature engineering Feature engineering is the process of creating new features or identifying relevant pre-existing ones that are significant in improving the model's predictive capability. This stage is also a part of data preprocessing since you're still trying to turn the data into a form that is suitable for particular types of algorithms and efficient for training ML models. A feature engineering process includes the following techniques: ● Feature extraction: This is the process of identifying and transforming the most significant features from raw data, which will help the algorithm focus on what matters in the dataset. For instance, what data will be useful if you want to build a model that predicts which student should be awarded a scholarship? Features like academic performance, financial background, personal attributes, etc, would be relevant in this case. Some feature extraction techniques include dimensionality reduction and Principal Component Analysis (PCA)—both of which can be implemented with Python's Scikit-Learn library. The technique you choose depends on the data type and your goal. ● Feature scaling or normalization or standardization: This is the process of normalizing (adjusting features to a common scale) the features in a dataset to ensure it is easier for the learning algorithms to find a meaningful relationship between them. When all the features in a dataset have a similar range, it eliminates bias due to the magnitude of the data. Do note that not all ML algorithms will require feature scaling. Tree-based algorithms that can handle many features, such as decision trees and random forests, do not need feature scaling. ● Feature encoding: This is the process of converting relevant categorical features into numerical features to ensure the algorithm performs at its best. For instance, if the observations in the "Financial Background" column of the scholarship prediction dataset are categorical, feature encoding converts them to numerical values of 0s and 1s. One-hot or dummy encoding, label encoding, and ordinal encoding are all examples of feature encoding. You can use Python's Scikit-Learn library for this process. Feature engineering is one of the most essential stages in the pipeline as it helps the ML model learn data patterns and improves its performance. It's a complex process and requires experimentation to determine the features that are relevant in training the model, depending on your specific use case. 4. Model training and testing After choosing the appropriate ML algorithm(s) based on your problem (such as classification, clustering, regression) and performance metrics, it's time to train the resulting model. The dataset is typically split into two categories for training and testing. The training dataset will help the model learn any underlying patterns and relationships within the features and the target variables (or labels). This training teaches the model to take an input and predict an output with the highest possible accuracy. Features are the inputs that provide information to the model, while the target variable (label) is the output the model tries to predict. When the model reaches the target prediction accuracy, then the training is concluded. It's time for the model to be tested. Using the testing dataset, the model will try to predict an output. If the model performance is below expectation, you can handle the underperformance by retraining the model, changing the algorithm, adding more accurate data, and engineering new features. 5. Model evaluation or analysis The model's performance has to be assessed after training using performance metrics such as accuracy, precision, recall, and F1-score. ● Accuracy: Accuracy shows the proportion of correctly classified instances out of all the instances. ● Precision: Precision shows how many instances the model classified as positive are actually positive. ● Recall: Recall measures how many of the actual positive instances the model managed to identify. ● F1-score: The F1-score balances both precision and recall into a single number. As precision increases, recall goes down, and vice versa. It’s a valuable metric when you want to find an optimal balance between precision and recall. The goal of this stage is to ensure that the model performs well with new, unseen data, not just your training dataset. 6. Model Deployment Getting to this stage in the pipeline means you've successfully developed and evaluated a model that meets your prediction accuracy level. If so, the model should then be deployed to a production environment to ensure it can function in a real-world setting. For example, the scholarship prediction model can be implemented within the school's existing academic record system so it can actively be used. TensorFlow Extended (TFX), an open-source tool released by Google, makes the model deployment process in Python efficient. This tool offers many frameworks, libraries, and components for model training, serving, deployments, and monitoring. This section is not exhaustive, so to learn more about how to approach the deployment of an ML project in Python read this article. 7. Model monitoring This is the final stage in an ML pipeline. As the data changes over time, the prediction accuracy of the model will reduce. This decline in accuracy is known as model drift. That's why it's crucial to continuously monitor the model's performance in production and retrain it when necessary to ensure it's still accurate and reliable. There are two main types of model drift: ● Data drift: This occurs when the statistical properties of the features change, but the relationship between the features and target variables remains constant. That means the features in production differ from those in the training stage. For instance, in the case of the scholarship prediction model, if the model was implemented before 2020 and the school introduces new admission standards and changes the way they evaluate extracurricular activities, it will affect the model's prediction capability. This is because the model hasn't been updated to reflect these changes, leading to poor performance. ● Concept drift: This occurs when the relationship between features and the target variable evolves over time. For instance, using the scholarship prediction model, if the original model was trained to predict scholarship likelihood based on GPA and test scores, but the school is now focusing on social values like community service or leadership potential, the model's accuracy will decline. This is because the relationship between the features and the target variable (scholarship award) has changed. So, the model needs to be retrained with the new criteria. Model monitoring can also track performance shifts, bias/fairness, and operation metrics. TensorBoard from the TensforFlow library in Python is a good tool for model monitoring. ML observability platforms such as Evidently AI and Valohai are also useful in this stage. Benefits of Creating a Machine Learning Pipeline Some advantages of creating a machine learning pipeline include: Better productivity: ML pipelines reduce the need for constant human intervention and manual approaches. By cutting down repetitive processes and prioritizing automation, data scientists get more time to do jobs that actually require human intervention such as decision making, data annotation (labeling data correctly), and fine-tuning models during training. High-quality predictions: A well-constructed ML pipeline reduces error margin. Thus, the model returns better predictions. Scalability: An efficient ML pipeline can handle large volumes of complex data while ensuring the models continue to perform effectively. This is very important because businesses are constantly growing, and so does their data size. Easy troubleshooting: Since every stage in the pipeline is independent of the other, it's easier to track issues in a particular stage and subsequently debug. Ultimately, ML pipelines are a powerful asset for data scientists. They provide a consistent and efficient sequence for turning raw data into valuable insights. Final Thoughts ML pipelines allow data scientists and engineers to fine-tune existing models and carry out constant performance evaluations continuously. It provides a standardized interface for managing, improving, and collaborating on ML projects. Building and running machine learning (ML) models is a rewarding yet time-consuming and complex process of data preparation, feature generation, model fitting, validation, and deployment stages. What's more, these models need to be updated frequently as trends in data shift. Otherwise, they become stale and make low-quality predictions. That's why an end-to-end ML pipeline is a must to automate workflows for scalability and efficiency. This pipeline makes it easier to develop, test, and deploy fresh models consistently. In this article, you'll learn about ML pipeline and how to create one for your needs. What Is a Machine Learning Pipeline? An ML pipeline is a systematic automation of the multiple stages of an ML workflow, with each step in the pipeline independent from the other but still aimed toward a collective outcome. This desired outcome is to go from raw data to high-quality predictions through multiple sequential steps, starting from data extraction and preprocessing to model training and deployment. A machine learning pipeline follows a modular approach. That is, each stage is an independent unit that functions separately. Yet, all units come together to get the final outcome. A machine learning pipeline follows a modular approach. That is, each stage is an independent unit that functions separately. Yet, all units come together to get the final outcome. Instead of manually collecting and processing data, training a model, validating its quality, and finally deploying it, an ML pipeline automates these repetitive processes. By doing so, the pipeline makes the management and maintenance of models more efficient and less error-prone, which ultimately improves the accuracy and reliability of these models. ML pipelines help data scientists and artificial intelligence (AI) engineers to efficiently manage the complexity of the ML process by providing them with a scalable and durable solution to develop, produce, and update AI systems. It can be used for single or multiple models. A well-executed pipeline makes the implementation of ML workflow more flexible. It also allows you define the required features, model parameters, and monitored metrics to produce and update the most crucial component of the pipeline: the model. However, do not let the word “pipeline” trick you into thinking it's a one-way flow because it isn't. ML pipelines are cyclic to enable iteration. Note: An ML pipeline is different from a data pipeline. The goal of a data pipeline is to move data between systems while transforming them. However, an ML pipeline is focused on streamlining and speeding up complex ML processes for greater efficiency. Note: An ML pipeline is different from a data pipeline. The goal of a data pipeline is to move data between systems while transforming them. However, an ML pipeline is focused on streamlining and speeding up complex ML processes for greater efficiency. A Step-by-Step Guide to Creating a Machine Learning Pipeline Irrespective of the use case, most ML pipelines follow the general ML workflow. Thus, there are some typical stages that are similar in most pipelines. Each of these stages builds upon the preceding stage. That is, the output of the prior stage becomes the input for the new stage until the final outcome is achieved . Below are the stages in a typical ML pipeline: the output of the prior stage becomes the input for the new stage until the final outcome is achieved 1. Data gathering or collection 1. Data gathering or collection This is the first stage in an ML pipeline. Here, raw data will be collected and recorded from sources such as Application Programming Interfaces (APIs), surveys and questionnaires, online databases, institutional records, files from government agencies, and many more. The data source can be primary (first-hand research) or secondary (existing resources), depending on the specifics of the ML use case. You can use any of the powerful data collection libraries in Python (Request, Beautiful Soup, Scrapy, and Selenium) for this stage. Meanwhile, because this data is raw, unstructured, and messy, it's not yet ready for ML analysis, hence the next stage. 2. Data preprocessing 2. Data preprocessing In this stage, the data will be cleaned and organized in a usable format for efficient analysis and model training and testing. If this stage is skipped, the data will be unsuitable for the model. That is, the model won't produce any tangible result with the dataset. Some data preprocessing steps in ML include sorting out missing data, handling duplicates, reducing noisy data, and feature engineering (which I discuss in the next stage). Pandas, NumPy, Scikit-Learn, and Scipy are helpful Python libraries for data preprocessing. The ultimate goal of data preprocessing is to prepare the data for feature engineering. 3. Feature engineering 3. Feature engineering Feature engineering is the process of creating new features or identifying relevant pre-existing ones that are significant in improving the model's predictive capability. This stage is also a part of data preprocessing since you're still trying to turn the data into a form that is suitable for particular types of algorithms and efficient for training ML models. A feature engineering process includes the following techniques: ● Feature extraction : This is the process of identifying and transforming the most significant features from raw data, which will help the algorithm focus on what matters in the dataset. Feature extraction For instance, what data will be useful if you want to build a model that predicts which student should be awarded a scholarship? Features like academic performance, financial background, personal attributes, etc, would be relevant in this case. Some feature extraction techniques include dimensionality reduction and Principal Component Analysis (PCA)—both of which can be implemented with Python's Scikit-Learn library. The technique you choose depends on the data type and your goal. ● Feature scaling or normalization or standardization : This is the process of normalizing (adjusting features to a common scale) the features in a dataset to ensure it is easier for the learning algorithms to find a meaningful relationship between them. Feature scaling or normalization or standardization When all the features in a dataset have a similar range, it eliminates bias due to the magnitude of the data. Do note that not all ML algorithms will require feature scaling. Tree-based algorithms that can handle many features, such as decision trees and random forests, do not need feature scaling. ● Feature encoding : This is the process of converting relevant categorical features into numerical features to ensure the algorithm performs at its best. For instance, if the observations in the "Financial Background" column of the scholarship prediction dataset are categorical, feature encoding converts them to numerical values of 0s and 1s. Feature encoding One-hot or dummy encoding, label encoding, and ordinal encoding are all examples of feature encoding. You can use Python's Scikit-Learn library for this process. Feature engineering is one of the most essential stages in the pipeline as it helps the ML model learn data patterns and improves its performance. It's a complex process and requires experimentation to determine the features that are relevant in training the model, depending on your specific use case. 4. Model training and testing 4. Model training and testing After choosing the appropriate ML algorithm(s) based on your problem (such as classification, clustering, regression) and performance metrics, it's time to train the resulting model. The dataset is typically split into two categories for training and testing. The training dataset will help the model learn any underlying patterns and relationships within the features and the target variables (or labels). This training teaches the model to take an input and predict an output with the highest possible accuracy. Features are the inputs that provide information to the model, while the target variable (label) is the output the model tries to predict. Features are the inputs that provide information to the model, while the target variable (label) is the output the model tries to predict. When the model reaches the target prediction accuracy, then the training is concluded. It's time for the model to be tested. Using the testing dataset, the model will try to predict an output. If the model performance is below expectation, you can handle the underperformance by retraining the model, changing the algorithm, adding more accurate data, and engineering new features. 5. Model evaluation or analysis 5. Model evaluation or analysis The model's performance has to be assessed after training using performance metrics such as accuracy, precision, recall, and F1-score. ● Accuracy : Accuracy shows the proportion of correctly classified instances out of all the instances. Accuracy ● Precision : Precision shows how many instances the model classified as positive are actually positive. Precision ● Recall : Recall measures how many of the actual positive instances the model managed to identify. Recall ● F1-score : The F1-score balances both precision and recall into a single number. As precision increases, recall goes down, and vice versa. It’s a valuable metric when you want to find an optimal balance between precision and recall. F1-score As precision increases, recall goes down, and vice versa. The goal of this stage is to ensure that the model performs well with new, unseen data, not just your training dataset. 6. Model Deployment 6. Model Deployment Getting to this stage in the pipeline means you've successfully developed and evaluated a model that meets your prediction accuracy level. If so, the model should then be deployed to a production environment to ensure it can function in a real-world setting. For example, the scholarship prediction model can be implemented within the school's existing academic record system so it can actively be used. TensorFlow Extended (TFX), an open-source tool released by Google, makes the model deployment process in Python efficient. This tool offers many frameworks, libraries, and components for model training, serving, deployments, and monitoring. This section is not exhaustive, so to learn more about how to approach the deployment of an ML project in Python read this article. 7. Model monitoring 7. Model monitoring This is the final stage in an ML pipeline. As the data changes over time, the prediction accuracy of the model will reduce. This decline in accuracy is known as model drift . That's why it's crucial to continuously monitor the model's performance in production and retrain it when necessary to ensure it's still accurate and reliable. model drift There are two main types of model drift: ● Data drift : This occurs when the statistical properties of the features change, but the relationship between the features and target variables remains constant. That means the features in production differ from those in the training stage. ● Data drift For instance, in the case of the scholarship prediction model, if the model was implemented before 2020 and the school introduces new admission standards and changes the way they evaluate extracurricular activities, it will affect the model's prediction capability. This is because the model hasn't been updated to reflect these changes, leading to poor performance. ● Concept drift : This occurs when the relationship between features and the target variable evolves over time. For instance, using the scholarship prediction model, if the original model was trained to predict scholarship likelihood based on GPA and test scores, but the school is now focusing on social values like community service or leadership potential, the model's accuracy will decline. This is because the relationship between the features and the target variable (scholarship award) has changed. So, the model needs to be retrained with the new criteria. Concept drift Model monitoring can also track performance shifts, bias/fairness, and operation metrics. TensorBoard from the TensforFlow library in Python is a good tool for model monitoring. ML observability platforms such as Evidently AI and Valohai are also useful in this stage. Benefits of Creating a Machine Learning Pipeline Some advantages of creating a machine learning pipeline include: Better productivity: ML pipelines reduce the need for constant human intervention and manual approaches. By cutting down repetitive processes and prioritizing automation, data scientists get more time to do jobs that actually require human intervention such as decision making, data annotation (labeling data correctly), and fine-tuning models during training. High-quality predictions: A well-constructed ML pipeline reduces error margin. Thus, the model returns better predictions. Scalability: An efficient ML pipeline can handle large volumes of complex data while ensuring the models continue to perform effectively. This is very important because businesses are constantly growing, and so does their data size. Easy troubleshooting: Since every stage in the pipeline is independent of the other, it's easier to track issues in a particular stage and subsequently debug. Better productivity : ML pipelines reduce the need for constant human intervention and manual approaches. By cutting down repetitive processes and prioritizing automation, data scientists get more time to do jobs that actually require human intervention such as decision making, data annotation (labeling data correctly), and fine-tuning models during training. Better productivity High-quality predictions : A well-constructed ML pipeline reduces error margin. Thus, the model returns better predictions. High-quality predictions Scalability : An efficient ML pipeline can handle large volumes of complex data while ensuring the models continue to perform effectively. This is very important because businesses are constantly growing, and so does their data size. Scalability Easy troubleshooting : Since every stage in the pipeline is independent of the other, it's easier to track issues in a particular stage and subsequently debug. Easy troubleshooting Ultimately, ML pipelines are a powerful asset for data scientists. They provide a consistent and efficient sequence for turning raw data into valuable insights. Final Thoughts ML pipelines allow data scientists and engineers to fine-tune existing models and carry out constant performance evaluations continuously. It provides a standardized interface for managing, improving, and collaborating on ML projects.