https://blog.sksoumik.com/tags/mlops/ Recent content in MLOps on Sadman Kabir Soumik Hugo -- gohugo.io en Copyright © 2022, Sadman Kabir Soumik Fri, 17 Feb 2023 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/airflow-machine-learning-pipeline/ Fri, 17 Feb 2023 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/airflow-machine-learning-pipeline/ Author: Sadman Kabir Soumik Let's first understand what's MLOps. What is MLOps? MLOps (Machine Learning Operations) is a set of practices and tools used to manage the entire lifecycle of machine learning models. MLOps includes everything from data preparation and model training to deployment, monitoring, and ongoing maintenance. The primary goal of MLOps is to create a streamlined and automated process for deploying and managing machine learning models at scale. https://blog.sksoumik.com/artificial-intelligence/machine-learning-model-compression-techniques/ Mon, 10 Oct 2022 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/machine-learning-model-compression-techniques/ There are 4 main approaches you can consider for model compression. They are: Quantization Pruning Knowledge Distillation Low-Rank Factorization Quantization Quantization is the most general and commonly used model compression method. Quantization reduces a model’s size by using fewer bits to represent its parameters. By default, most software packages use 32 bits to represent a float number (single precision floating point). If a model has 100M parameters and each requires 32 bits to store, it’ll take up 400 MB.