How to Build an End to End Machine Learning Pipeline?

Machine Learning Pipeline Architecture| Building an End-to-End ML Pipeline

How to Build an End to End Machine Learning Pipeline?
 |  BY Daivi

What is a Machine Learning Pipeline?

A machine learning pipeline helps automate machine learning workflows by processing and integrating data sets into a model, which can then be evaluated and delivered. A well-built pipeline helps in the flexibility of the model implementation. A pipeline in machine learning is a technical infrastructure that allows an organization to organize and automate machine learning operations.

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The logic of the pipeline and the range of tools it incorporates varies based on the business requirements. The machine learning pipeline offers data scientists a way to handle data for training, orchestrate models, and monitor them in deployment.

How are End-to-End Machine Learning Pipelines Transforming Businesses?

The machine learning data pipeline helps identify patterns in given data, which leads businesses to better decision-making. The machine learning pipeline boosts the machine learning model's performance leading to more efficient model deployment and better management of the models.

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Here are some significant advantages of implementing a data pipeline in machine learning-


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As the machine learning process evolves, you need to repeat many aspects of the machine learning pipeline throughout the organization. You can configure your model deployment to handle those frequent algorithm-to-algorithm calls, and this ensures that the correct algorithms are running smoothly and computation time is minimal.

Although you require different models for different purposes, you can use the same functions/processes to build those models. This makes it easier for machine learning pipelines to fit into any model-building application.

By optimizing specific business approaches, machine learning helps in providing customer behavior insights. Machine learning algorithms make big data processing faster and make real-time model predictions extremely valuable to enterprises.

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Introduction to the Machine Learning Pipeline Architecture

There are various stages in a machine learning pipeline architecture, mainly- Data preprocessing, Model training, Model evaluation, and Model deployment. Each stage of the data pipeline passes processed data to the next step, i.e., it gives the output of one phase as input data into the next phase. 

  • Data Preprocessing- This step entails collecting raw and inconsistent data selected by a team of experts. The pipeline processes the raw data into an understandable format. Data processing techniques include feature extraction, feature selection, dimensionality reduction, sampling, etc. The final sample used for training and testing the model is the output of data preprocessing.

  • Model Training- Selecting an appropriate machine learning algorithm for model training is crucial in a machine learning pipeline architecture. A mathematical algorithm specifies how a model will detect patterns in data.

  • Model Evaluation- The sample models are trained and tested on historical data to make predictions and choose the best-performing model for the next step.

  • Model Deployment- The final step is to deploy the machine learning model to the production line. Ultimately, the end-user can obtain predictions based on real-time data.

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How to Build an End-to-End a Machine Learning Pipeline?

There are mainly seven stages of building an end-to-end pipeline in machine learning. Let us look at each of these stages-

The initial stage in every machine learning workflow is transferring incoming data into a data repository. The vital element is that data is saved without alteration, allowing everyone to record the original information accurately. You can obtain data from various sources, including pub/sub requests. Also, you can use streaming data from other platforms. Each dataset has a separate pipeline, which you can analyze simultaneously. The data is split within each pipeline to take advantage of numerous servers or processors. This reduces the overall time to perform the task by distributing the data processing across multiple pipelines. For storing data, use NoSQL databases as they are an excellent choice for keeping massive amounts of rapidly evolving organized/unorganized data. They also provide storage space that is shared and extensible.

This time-consuming phase entails taking input, unorganized data and converting it into data that the models can use. During this step, a distributed pipeline evaluates the data's quality for structural differences, incorrect or missing data points, outliers, anomalies, etc., and corrects any abnormalities along the way. This stage also includes the process of feature engineering. Once you ingest data into the pipeline, the feature engineering process begins. It stores all the generated features in a feature data repository. It transfers the output of features to the online feature data storage upon completion of each pipeline, allowing for easy data retrieval.

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The primary objective of a machine learning data pipeline is to apply an accurate model to data that it hasn't been trained on, based on the accuracy of its feature prediction. To assess how the model works against new datasets, you need to divide the existing labeled data into training, testing, and validation data subsets at this point. Model training and assessment are the next two pipelines in this stage, both of which should be likely to access the API used for data splitting. It needs to produce a notification and return with the dataset to protect the pipeline (model training or evaluation) against selecting values that result in an irregular data distribution.

This pipeline includes the entire collection of training model algorithms, which you can use repeatedly and alternatively as needed. The model training service obtains the training configuration details, and the pipeline's process requests the required training dataset from the API (or service) constructed during the data splitting stage. Once it sets the model, configurations, training parameters, and other elements, it stores them into a model candidate data repository which will be evaluated and used further in the pipeline. Model training should take error tolerance, data backups, and failover on training segments. For example, you can retrain each split if the latest attempt fails, owing to a transitory glitch.

This stage assesses the stored models' predictive performance using test and validation data subsets until a model solves the business problem efficiently. The model evaluation step uses several criteria to compare predictions on the evaluation dataset with actual values. A notification service is broadcast once a model is ready for deployment, and the pipeline chooses the "best" model from the evaluation sample to make predictions on future cases. A library of multiple evaluators provides the accuracy metrics of a model and stores them against the model in the data repository.

Once the model evaluation is complete, the pipeline selects the best model and deploys it. The pipeline can deploy multiple machine learning models to ensure a smooth transition between old and new models; the pipeline services continue to work on new prediction requests while deploying a new model.

The final stage of a pipeline in machine learning is model monitoring and performance scoring. This stage entails monitoring and assessing the model behavior on a regular and recurring basis to gradually enhance it. Models are used for scoring based on feature values imported by previous stages. When a new prediction is issued, the Performance Monitoring Service receives a notification, runs the performance evaluation, records the outcomes, and raises the necessary alerts. It compares the scoring to the observed results generated by the data pipeline during the assessment. You can use various methods for monitoring, the most common of which is logging analytics (Kibana, Grafana, Splunk, etc.).

Machine Learning Pipeline Tools

A machine learning pipeline uses hundreds of tools, libraries, and frameworks. Sometimes, it becomes difficult for companies to hire a separate data science team for building machine learning pipelines using various resources. This is where machine learning pipeline tools come into the picture. Businesses involved with data processing tasks but running low on a budget require machine learning pipeline tools to improve their performance and efficiency.

A machine learning pipeline tool handles the development, maintenance, and tracking of data processing pipelines. Machine learning pipeline tools help businesses streamline their data usage, resulting in better decision-making and increased overall productivity.

How do Machine Learning Pipeline Tools Benefit Businesses?

  • Accurate Machine Learning Models- Automated machine learning pipeline technologies can provide a continuous range of high data that will aid in fine-tuning your machine learning algorithms. It creates better machine learning models that will generate more accurate predictions.

  • Faster Deployment- Data pipeline automation accelerates the process of training, testing, and refining machine learning models, allowing you to deploy them sooner in the market.

  • Enhanced Business Forecasting- You may improve your business forecasting abilities by using data pipeline technologies that help you construct a better machine learning model. Improved business forecasting enables you to stay ahead of the competition, provide a better client experience, and reap business profits.

Now that you are aware of the benefits of machine learning pipeline tools let us look at a few popular tools used in building an end-to-end machine learning pipeline-

  1. MLFlow 

MLflow is a free and open-source tool for managing machine learning workflow, including experimentation, production, deployment, and a centralized model repository. It has four elements: tracking, projects, models, and registration. Individuals and organizations of any scale can benefit from MLflow. The tool is not reliant on any particular library or a programming language and can be combined with any machine learning library.

  1. DVC

Data Version Management, or DVC, is an experimental tool that helps define your pipeline irrespective of the programming language used. DVC enables you to save time when discovering a bug in earlier versions of your ML model by utilizing code, data versioning, and reproducibility. For machine learning applications, DVC is an open-source version control system. It keeps track of data sets and machine learning models, making them more shareable and replicable. DVC handles large files, data sets, machine learning models, metrics, and code.

  1. Neptune

Neptune is a machine learning metadata repository designed for monitoring various experiments by research and production teams. It comes with a customizable metadata format that lets you organize training and production info any way you desire. All model building metadata may be logged, stored, shown, managed, compared, and queried in one place. It's similar to a dictionary or folder layout that you build in code and then present in the user interface.

  1. Polyaxon

Polyaxon is a Kubernetes machine learning platform for recreating and managing machine learning workflows. Polyaxon can host and maintain the tool, implemented in any data center or cloud provider. Polyaxon's orchestration lets you get the most out of your cluster by managing jobs and experiments through CLI, dashboard, SDKs, and REST API. Large-scale deep learning applications can be built, trained, and monitored using the platform. It supports major deep learning frameworks like Torch, Tensorflow, and MXNet.

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Machine Learning Pipeline Deployment on Different Platforms

This section gives you an overview of deploying machine learning data pipelines on various platforms such as Azure, AWS, etc. It also consists of some projects which will provide you with a better idea of deploying machine learning pipelines on these platforms.

Azure Machine Learning Pipelines

The Azure Machine Learning Pipeline makes creating, monitoring, and enhancing machine learning processes easier. It is easy to use and consists of various other pipelines, each of which has a function. Some of the multiple benefits of an Azure machine learning data pipeline are-

  • The Azure Machine Learning pipeline enables the coordination of several pipelines with diverse and extensible computation and storage facilities. Individual pipeline phases are run on separate compute units to use existing compute resources.

  • It enables the creation of pipeline layouts for specific instances to activate published pipelines from multiple systems, allowing for reusability.

  • Azure machine learning pipelines optimize productivity by constantly monitoring data and result pathways.

 

Here are some Azure MLOps projects you can practice to gain hands-on experience working with Azure Machine Learning Pipelines -

 

  • Azure Text Analytics for Medical Search Deployment

This project develops a machine learning application to recognize the relationship and pattern between various medical terms. It will illustrate how to build an intelligent search engine that will scan for documents that contain those keywords. The project also entails creating an Azure machine learning pipeline to deploy and extend the application. This project will introduce you to various Azure services, including Azure Data Storage, Data Factory, databricks, and Azure Containers, among others.

 

Source Code- Azure Text Analytics for Medical Search Deployment

 

In this MLOps Azure project, you'll learn how to use scalable CI/CD ML pipelines to deploy a classification machine learning model on Azure to predict a customer's licensing status. It will assist you in comprehending Azure DevOps and developing a classification model that will forecast the licensing status. You'll also learn how to use Azure DevOps to implement the license status classification model in a scalable manner. The dataset contains customer data whose license status is forecasted, such as granted, updated, or terminated.

 

Source Code- MLOps using Azure DevOps to Deploy a Classification Model

 

  • Azure Deep Learning-Deploy RNN CNN models for TimeSeries

 

You will learn how to do Docker-based deployment of RNN and CNN Models for Time Series Forecasting on Azure Cloud in this Azure MLOps Project. Use Azure as your cloud platform, and Microsoft Azure's ecosystem includes several robust services for building an end-to-end MLOps pipeline. In this project, you will deploy your time-series deep learning model on the Azure cloud platform in a multi-part manner.

 

Source Code- Azure Deep Learning-Deploy RNN CNN models for TimeSeries

AWS Machine Learning Pipelines

AWS machine learning data pipelines allow businesses to develop, test, and deploy Machine Learning models at volume. Data transformation, feature extraction, data retrieval, model assessment and evaluation, and model deployment are all part of this process.

 

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Here are some AWS MLOps project ideas you can try your hands on-

 

  • ML Model Deployment on AWS for Customer Churn Prediction

This MLOps project idea seeks to deploy a model that predicts if a client will churn in the coming days or not. AWS (Amazon Web Services) is the cloud provider in this project. This project will introduce you to Terraform and show you how to deploy your machine learning model on the Gunicorn web server. It will teach you to store the Terraform state in the AWS s3 backend bucket.

 

Source Code- ML Model Deployment on AWS for Customer Churn Prediction

 

  • AWS MLOps Project to Deploy Multiple Linear Regression Model

This project aims to create a cost-optimized machine learning pipeline for a time series multiple linear regression model on the AWS cloud platform (Amazon Web Services). Working on this project will introduce you to the concept of Docker, Lightsail, and Flask. It will help you understand the EC2 machine setup and deploy the machine learning application on Lightsail.

 

Source Code- AWS MLOps Project to Deploy Multiple Linear Regression Model

 

Machine learning data pipelines play a crucial role in making businesses stand out in the industry. These pipelines save up a lot of time and enhance the overall efficiency of machine learning operations in an organization. You can learn more about how ML pipelines work by practicing a variety of solved end-to-end MLOps projects from the ProjectPro repository. 

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FAQs

What tools exist for managing data science and machine learning pipelines?

There are various tools available for managing data science and machine learning pipelines such as MLFlow, Optuna, Polyaxon, DVC, Amazon Sagemaker, Cortex, etc.  

Is python suitable for machine learning pipeline design patterns?

Python is one of the best choices for machine learning pipelines as it includes many libraries which support the operations involved in the machine learning pipeline.

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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