Dockerizing

In the rapidly evolving domain of machine learning (ML), the ability to seamlessly package and deploy models is as crucial as the development of the models themselves. Containerization has emerged as the game-changing solution to this, offering a streamlined path from the local development environment to production. Docker, a leading platform in containerization, provides the tools necessary to encapsulate ML applications into portable and scalable containers. This article delves into the step-by-step process of…

Learn How To Build a Portable and Scalable Data Analysis Environment with Docker-Compose And VolumesPhoto by Tom Fisk On PexelsDocker has revolutionised the way we deploy and manage our applications and data analysis tools. It allows for effortless scaling, and the ability to easily customise the services to suit specific needs.In this tutorial, I will show you how to quickly deploy Apache Zeppelin and Apache Spark using a docker-compose.yaml file and take advantage of volumes to manage data dependencies among…

Guide On Deploying ML Models With Flask and Containerizing Your WorkImage from Unsplash by Jonas SmithDeploying ML models is an essential step in the ML Lifecycle that’s often overlooked by Data Scientists. Without model deployment/hosting, there is no usage of Machine Learning models in real-world applications. There’s a variety of ways to host your ML models and one of the simplest, yet most effective is Flask.Flask is a micro-web framework that’s implemented in Python. Using Flask, we’ll walk through an example of how…