From Data Science to Production: Streamlining Model Deployment in Cloud Environment

Author(s): Wencong Yang

Originally published on Towards AI.

Image by author (Ideogram)

In the realm of data science projects, the excitement lies in the “Intelligent” aspect, where deep learning models successfully make remarkably accurate predictions. However, the transition to the “Engineering” phase, involving the deployment of these models into production environments, can be a tedious task for data scientists. This phase demands time-consuming and meticulous configuration of software and hardware.

Fortunately, today’s software development ecosystem offers a range of options for deploying AI models, from complex setups on personal servers to streamlined model-as-a-service solutions. Data scientists, unlike dedicated AI infrastructure engineers, often seek a rapid deployment approach while maintaining the requisite level of web service quality. This entails fulfilling several key requirements:

Establishing the execution environment (including OS system and dependencies) once and for all.Ensuring separation between deployment configuration and application code.Providing a publicly accessible URL to invoke the model.Eliminating the need for server provisioning and infrastructure management.Ensuring scalability in computing resources while remaining cost-effective.

The solution? Launching a containerized application on a serverless cloud platform. This article serves as a step-by-step guide to expedite deployment, focusing on two fundamental components:

Docker: For creating containerized applications.AWS Fargate: A serverless cloud computing product for running containerized workloads.

We’ll use a real-world project as an example… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI