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

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

Last Updated on April 2, 2024 by Editorial Team

Author(s): Wencong Yang

Originally published on Towards AI.


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In the realm of data science projects, the excitement lies in the β€œIntelligent” aspect, where deep learning models successfully make remarkably accurate predictions. However, the transition to the β€œEngineering” 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.

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