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Model deployment on unix for beginners

Track :

Computer Science

Lessons no : 7

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Lessons | 7

Importance of unix and big data in Data science Big data and Unix in Data Science

Importance of unix and big data in Data science Big data and Unix in Data Science

Installing Ubuntu VM on windows machine step by step How to install ubuntu on windows 10

Installing Ubuntu VM on windows machine step by step How to install ubuntu on windows 10

Windows vs Linux Operating System Which operating system is best Windows or Linux

Windows vs Linux Operating System Which operating system is best Windows or Linux

Machine Learning Model Deployment Pointers model deployment machine learning model deployment

Machine Learning Model Deployment Pointers model deployment machine learning model deployment

Machine Learning Model Deployment on Unix Part 1 Machine learning model deployment step by step

Machine Learning Model Deployment on Unix Part 1 Machine learning model deployment step by step

Machine Learning Model Deployment On Unix Part 2 Machine learning Model Deployment

Machine Learning Model Deployment On Unix Part 2 Machine learning Model Deployment

Machine Learning Model Deployment On Unix Part 3 Machine learning Model Deployment

Machine Learning Model Deployment On Unix Part 3 Machine learning Model Deployment

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Model deployment on unix course, in this course dives into the essentials of deploying machine learning models on Unix systems, covering the entire process from preparing the environment to launching and monitoring models in production. You'll learn how to package models, create APIs using tools like Flask or FastAPI, and automate deployment with shell scripting and Docker. The course also explores scaling deployments with Kubernetes, securing environments, and optimizing performance. Through practical examples, you'll master key Unix commands, scheduling with Crontab, and integrating monitoring tools. By the end, you'll be equipped to efficiently deploy and manage machine learning models on Unix platforms, ready for real-world applications.