---
title: "Discussion on \"Day 18 - Deploy and Serve Model for Inference using AWS SageMaker\" | Hashnode"
description: "Discussion on \"Day 18 - Deploy and Serve Model for Inference using AWS SageMaker\". From Model Artifact to Live Prediction Endpoint\nIn the previous step (Day 17), we:\n\ntrained a model\n\nsaved it as an artifact\n\nuploaded it to S3\n\n\nNow we move to the most important stage:\n\nDeploying th"
url: "https://hashnode.com/posts/day-18-deploy-and-serve-model-for-inference-using-aws-sagemaker/6a016274786d9353ddcdb08d"
---

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# [Day 18 - Deploy and Serve Model for Inference using AWS SageMaker](https://prasadsuman.hashnode.dev/day-18-deploy-and-serve-model-for-inference-using-aws-sagemaker?utm_source=hashnode&utm_medium=feed)

From Model Artifact to Live Prediction Endpoint In the previous step (Day 17), we: trained a model saved it as an artifact uploaded it to S3 Now we move to the most important stage: Deploying th

May 11 · 4 min read · prasadsuman.hashnode.dev  
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## Written by

[Prasad Suman Mohan](https://hashnode.com/@sumanprasad)

@sumanprasad · India

  
[View profile](https://hashnode.com/@sumanprasad)

AWS Community Builder ❤| Cloud DevOps Engineer | Technical Writer✍️| 330+ Days of Consistent Community Contribution🌟|AWS Cloud Specialist |

👋 Hello! I'm passionate about DevOps and have over 1+ years of experience in the field. I'm proficient in a variety of cutting-edge technologies and always motivated to expand my knowledge and skills. Let's connect and grow together! SKILLS: 🔹 Languages & Runtimes: Python, Shell Scripting, HCL, YAML 🔹 Cloud Technologies: AWS, Microsoft Azure, GCP 🔹 Infrastructure Tools: Docker, Terraform, AWS CloudFormation 🔹 Other Tools: Linux, Git and GitHub Actions, Jenkins, Jira, GitLab (beginner), Docker, AWS DevOps 🔹 Web Development: HTML, CSS, Bootstrap, Python, SQL Job & Responsibilities: 🚀 Improved development efficiency by implementing CI/CD pipelines, resulting in a 30% reduction in deployment time on the test server. 🔒 Strengthened deployment and testing reliability by utilizing Docker containers and optimizing Dockerfile, reducing development issues on the test server by 20%. ⚙️ Automated S3 bucket log creation with Shell scripting, eliminating 100% of manual search and saving 2 hours per week. 📅 Scheduled EC2 instance start/stop using Lambda functions and Event Bridge, leading to a 25% decrease in infrastructure costs. 🔧 Utilized AWS, Linux, Python, Docker, Shell scripting, Terraform, Jenkins Pipelines, and automation to streamline workflows and improve overall system performance. I'm very detail-oriented and possess strong written and verbal communication skills. As a high performer with a possibility mindset, I strive to solve problems using efficient approaches. Let's Connect & Grow: If you find my profile suitable for the role you are searching for, please feel free to reach out to me at sumanprasad9766@gmail.com.

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