Mlops

Quick turnarounds are more than just tools — Operations tempo is the speed and efficiency at which an organization completes its operations or tasks. In MLOps, this can include tasks like model development, retraining deployment, maintenance, etc. A well-managed ops tempo helps deliver projects on time, keeps costs in check, reduces development time, and ensures high-quality results in…

A whirlwind tour of Machine Learning Lifecycle Management — Have you ever found yourself training models, tuning hyperparameters, and selecting features for hours only to realize you’ve already had a good set of parameters but forgot to track them or save the model? I know I have, maybe more often than I’d like to admit. Before you open a…

I am sure, everyone who knows something about DevOps is familiar with the so-called "DTAP" pipeline (Development > Testing > Acceptance > Production) which can be described as the following: Development: the most unstable environment where the development of the product happens. Does not have access to production data. …

In this article I will present a way of automated training of Machine Learning model. Tech stack will include Python, Docker, Github Actions, AWS (S3, Lambda, ECR and Fargate task). First let’s start with the upper part of the architecture, CI/CD part. We want to enable our data scientists to…

Deploying machine learning models using flask ever, checking out for the easy approach, along side checking other approaches.

Kubenetes model inferencing made easy. — In the wake of the release of chatGPT, it is getting increasing difficult to avoid technologies that leverages machine learning. From text prediction on your messaging app to facial recognition on your smart door bell, machine learning (ML) can be found in almost every piece of tech we use today. …

Machine Learning (ML) has become a vital part of modern enterprises, driving innovation and providing valuable insights. However, developing and deploying ML applications can be challenging, especially when managing the lifecycle of data, models, and code. This is where MLOps and DevOps come into play. We will explore how MLOps…

This article aims to serve as a guide for both beginners and seasoned professionals in the field of MLOps, showcasing a hand-picked selection of GitHub repositories that cover a wide spectrum of MLOps topics. Whether you’re looking for tools to streamline data preprocessing, frameworks for model deployment, or resources to…

Authors: Abhishek Bose, Chaitanya Basava Swiggy’s internal serving platform, the Data Science Platform or DSP, has evolved over the last few years by providing support for different inference frameworks, including MLEAP and Tensorflow. …