--- title: Welcome to the Production Grade ML Systems Series! description: An introduction to a 10-part series on building production-grade ML systems, using a real-time food delivery ETA prediction engine as the central project. The... canonical: https://daily.dev/posts/welcome-to-the-production-grade-ml-systems-series--msseux6aj twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Welcome to the Production Grade ML Systems Series! | daily.dev og:description: An introduction to a 10-part series on building production-grade ML systems, using a real-time food delivery ETA prediction engine as the central project. The... og:url: https://daily.dev/posts/welcome-to-the-production-grade-ml-systems-series--msseux6aj og:image: https://api.daily.dev/og/posts/MSseuX6aJ.png og:image:alt: Welcome to the Production Grade ML Systems Series! og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Welcome to the Production Grade ML Systems Series! **[Medium](https://daily.dev/sources/medium_js)** · 2 min read · 0 upvotes · 0 comments ## Summary An introduction to a 10-part series on building production-grade ML systems, using a real-time food delivery ETA prediction engine as the central project. The series covers the full MLOps stack: feature engineering with PySpark, experiment tracking with MLflow, low-latency model serving, real-time streaming with Kafka, automated retraining with Airflow, containerization with Docker, cloud deployment on AWS ECS, and model drift monitoring. The goal is to bridge the gap between notebook-based ML and real-world production deployments capable of hundreds of predictions per second at sub-10ms latency. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Machine Learning in Production? What This Really Means](https://daily.dev/posts/machine-learning-in-production-what-this-really-means-5olttb3bj) · Towards Data Science · 0 upvotes · 0 comments - [Model Development and Optimization for Production \(With Implementation\)](https://daily.dev/posts/model-development-and-optimization-for-production-with-implementation--zjdcni1ed) · Daily Dose of Data Science \| Avi Chawla \| Substack · 4 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#mlops](https://daily.dev/tags/mlops), [#apache-kafka](https://daily.dev/tags/apache-kafka), [#apache-airflow](https://daily.dev/tags/apache-airflow), [#pyspark](https://daily.dev/tags/pyspark) [View this post on daily.dev](https://daily.dev/posts/welcome-to-the-production-grade-ml-systems-series--msseux6aj) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Welcome to the Production Grade ML Systems Series!","url":"https://daily.dev/posts/welcome-to-the-production-grade-ml-systems-series--msseux6aj","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/welcome-to-the-production-grade-ml-systems-series--msseux6aj"},"datePublished":"2026-07-18T08:11:10.076Z","dateModified":"2026-09-13T19:27:34.817Z","description":"An introduction to a 10-part series on building production-grade ML systems, using a real-time food delivery ETA prediction engine as the central project. 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