--- title: "Building a Machine Learning Data Pipeline: Best Practices & Strategies" description: Master data pipeline strategies for successful machine learning. Optimize from data collection to model deployment for ultimate performance. image: https://www.harrisonclarke.com/hubfs/Building%20a%20Data%20Pipeline%20for%20Machine%20Learning%20-%20Banner.jpg --- [![Harrison Clarke](https://www.harrisonclarke.com/hubfs/HarrisonClarke_March2021/Images/main-site-logo.svg "Harrison Clarke")](https://www.harrisonclarke.com) - Our Focus ### Our Focus Our expertise in key areas that are critical for the success of your business. ![Group 10450](https://www.harrisonclarke.com/hubfs/Group%2010450.svg) Cloud [ About Cloud ](https://www.harrisonclarke.com/cloud) [ DevOps ](https://www.harrisonclarke.com/devops) [ Platform Engineering ](https://www.harrisonclarke.com/platform-engineering) [ Performance Engineering ](https://www.harrisonclarke.com/performance-engineering) [ Site Reliability Engineering ](https://www.harrisonclarke.com/site-reliability-engineering) [ DevSecOps ](https://www.harrisonclarke.com/devsecops) ![AI Submenu](https://www.harrisonclarke.com/hubfs/Data%20%26%20AI%20Submenu.svg) Data & AI [ About Data & AI ](https://www.harrisonclarke.com/data-and-ai) [ MLSecOps ](https://www.harrisonclarke.com/mlsecops) [ AI Research ](https://www.harrisonclarke.com/ai-research) [ Data ](https://www.harrisonclarke.com/data) [ Machine Learning ](https://www.harrisonclarke.com/machine-learning) [ MLOps ](https://www.harrisonclarke.com/mlops) - Our Services ### Our Services Tailored services for clients and candidates to build long term relationships. 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A data pipeline is a series of steps taken to process raw data into a form suitable for machine learning models. This includes tasks such as data ingestion, data preparation, and feature engineering. In this blog post, we will discuss best practices and strategies for building a successful data pipeline for machine learning. --- ### **Data Ingestion** **![Building-a-Data-Pipeline-for-Machine-Learning--Best-Practices-and-Strategies-](https://www.harrisonclarke.com/hs-fs/hubfs/Building-a-Data-Pipeline-for-Machine-Learning--Best-Practices-and-Strategies-.jpg?width=728&height=417&name=Building-a-Data-Pipeline-for-Machine-Learning--Best-Practices-and-Strategies-.jpg)** The first step in building a data pipeline is the ingestion of the raw data. This involves obtaining the raw data from its source and storing it in an appropriate format for further processing. It’s important to note that not all raw datasets are suitable for [machine learning](https://www.harrisonclarke.com/mlops), so it’s important to ensure that the dataset meets certain requirements before further processing can take place. For example, it should contain enough samples (rows) with enough features (columns) to be useful for training models. Additionally, the features should be correctly scaled or normalized so they can be meaningfully compared against each other. ### **Data Preparation** ![Exploring the Future of Platform Engineering - Image Blog 2-1](https://www.harrisonclarke.com/hs-fs/hubfs/Exploring%20the%20Future%20of%20Platform%20Engineering%20-%20Image%20Blog%202-1.jpg?width=1500&height=860&name=Exploring%20the%20Future%20of%20Platform%20Engineering%20-%20Image%20Blog%202-1.jpg) Once the raw dataset has been ingested, it needs to be prepared for further processing by cleaning and formatting it appropriately. This process can involve removing duplicate values or outliers that may skew results; transforming categorical variables into numerical ones; filling in any missing values; and normalizing or scaling numeric variables so they have a mean of 0 and standard deviation of 1. By performing these steps on the dataset beforehand, you can ensure that your models have access to clean and consistent input data which will lead to better results downstream. ### **Feature Engineering** ![Exploring the Future of Platform Engineering - Image Blog 2 copy](https://www.harrisonclarke.com/hs-fs/hubfs/Exploring%20the%20Future%20of%20Platform%20Engineering%20-%20Image%20Blog%202%20copy.jpg?width=1500&height=860&name=Exploring%20the%20Future%20of%20Platform%20Engineering%20-%20Image%20Blog%202%20copy.jpg) Feature engineering is one of the most important aspects of building a successful [machine learning model](https://www.harrisonclarke.com/blog-2023/understanding-the-benefits-of-mlops-for-ai-development) because it involves taking existing features from the dataset and transforming them into new features that are more meaningful and predictive of certain outcomes. Feature engineering techniques include creating polynomial combinations between variables, one-hot encoding categorical variables, discretizing continuous variables, generating synthetic samples from existing ones, etc. It’s important to understand your domain knowledge when performing feature engineering so you can create meaningful features based on prior experience rather than randomly generating them without any context or understanding behind them. Building a successful data pipeline for [machine learning](https://www.harrisonclarke.com/mlops) requires careful planning and execution at each stage of the process—from ingesting raw datasets to preparing them with cleaning and formatting steps; selecting relevant features through feature engineering; training models with quality input datasets; validating model performance; deploying models into production environments; monitoring performance over time; making changes as needed; etc.—all while meeting business objectives such as cost savings or increased efficiency goals in order to ensure success in building an effective [machine learning system](https://www.harrisonclarke.com/blog-2023/understanding-the-benefits-of-mlops-for-ai-development). By following best practices outlined above throughout this process, software engineers, CEOs & CTOs alike can help their organizations leverage powerful technology tools like machine learning quickly and effectively with minimal disruption or risk involved in doing so.   --- [![Work with the experts at Harrison Clarke](https://no-cache.hubspot.com/cta/default/6308956/cbfae56d-fcd6-4ecf-a046-30068f5149bd.png)](https://cta-redirect.hubspot.com/cta/redirect/6308956/cbfae56d-fcd6-4ecf-a046-30068f5149bd) SHARE - [**](https://twitter.com/intent/tweet?original_referer=https://www.harrisonclarke.com/blog/building-a-data-pipeline-for-machine-learning&url=https://www.harrisonclarke.com/blog/building-a-data-pipeline-for-machine-learning&source=tweetbutton&text=Building%20a%20Machine%20Learning%20Data%20Pipeline:%20Best%20Practices%20&%20Strategies) - [**](http://www.linkedin.com/shareArticle?mini=true&url=https://www.harrisonclarke.com/blog/building-a-data-pipeline-for-machine-learning) [MLOps](https://www.harrisonclarke.com/blog/tag/mlops) [Data/AI](https://www.harrisonclarke.com/blog/tag/data-ai) ![HC_Logomark_white](https://www.harrisonclarke.com/hubfs/HC_Logomark_white.svg) SUBSCRIBE Get the latest news from Harrison Clarke ### TRENDING ARTICLES [ Cloud Mastering Concurrency: A Guide for Software Engineers ](https://www.harrisonclarke.com/blog/mastering-concurrency-a-guide-for-software-engineers) [ Staffing tips I’m not a Tech Lead; I’m an Architect and vice versa ](https://www.harrisonclarke.com/blog/im-not-a-tech-lead-im-an-architect-and-vice-versa) [ Cloud Women in DevOps and the Importance of Diversity in DevOps Culture ](https://www.harrisonclarke.com/blog/women-in-devops-and-the-importance-of-diversity-in-devops-culture) [![Follow us on Linkedin - side banner](https://no-cache.hubspot.com/cta/default/6308956/94673da1-6733-49af-a854-b581c0d9397d.png)](https://cta-redirect.hubspot.com/cta/redirect/6308956/94673da1-6733-49af-a854-b581c0d9397d) [![New call-to-action](https://no-cache.hubspot.com/cta/default/6308956/732a4994-c2ee-497b-942f-90530e1ad75b.png)](https://cta-redirect.hubspot.com/cta/redirect/6308956/732a4994-c2ee-497b-942f-90530e1ad75b) Read also Read also BLOG Mastering MLOps: Best Practices for Secure Machine Learning Systems February 9, 2024 In today's digital landscape, data is hailed as the new oil, and artificial intelligence (AI) its refining process. For technology companies,... [Read More](https://www.harrisonclarke.com/blog/mastering-mlops-best-practices-for-secure-machine-learning-systems) BLOG Big Data's Impact: Optimizing AI with Vast Datasets December 5, 2023 In the dynamic realm of technology, the fusion of Big Data and [Artificial Intelligence (AI)](https://www.harrisonclarke.com/data-and-ai) is reshaping industries and unlocking unparalleled... [Read More](https://www.harrisonclarke.com/blog/big-datas-impact-optimizing-ai-with-vast-datasets) BLOG MLOps for Deep Learning: Best Practices and Tools August 22, 2023 In the current era of [data science](https://www.harrisonclarke.com/data), the development of powerful [deep learning models](https://www.harrisonclarke.com/deep-learning) has been made possible by the emergence of sophisticated... 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