2024-06-01 | #deep learning #machine learning #ml model #model #model compression #quantization
It has been a long time since I’ve shared any post so I thought today is the time for that :) In this post I’ll walk you through the details between static vs dynamic quantization which I think might be interesting.
2024-02-18 | #aws neuron #inferentia #ml model #ml model optimization
I had a chance to work with AWS Neuron SDK to optimize ML models to enable running inference endpoints Inferentia based instances. In this post, I give some insights about Neuron and how to optimize models using Neuron SDK.
2024-01-01 | #deployment #machine learning #ml model #mlops #techniques
In this first post of 2024, I wanted to give some quick insights about the details of MLOps and ML model deployment, and then different techniques used in deployment. MLOps, short for Machine Learning Operations, is a practice within the field of data science and machine learning that brings together the principles of DevOps and applies them to the unique challenges of machine learning model development and deployment.
2023-12-25 | #dynamic batch inference #ml #ml model serving #torchserve
Recently I’ve done research on ML model serving frameworks and worked on dynamic batch inference applications. I thought it would be great to share some details here which you might find useful.
2023-12-23 | #deep learning #ml model #model #model compression #quantization
Machine learning enables computers to perform tasks smartly by learning from data and instances, instead of just following fixed rules. This is enabled by the vast quantities of data gathered in different sectors and the rapid development in computing power, which collectively bolster the capabilities of machine learning algorithms.
