---
title: "Prasad Suman Mohan  (@sumanprasad) | Hashnode"
description: "AWS Community Builder ❤| Cloud DevOps Engineer | Technical Writer✍️| 330+ Days of Consistent Community Contribution🌟|AWS Cloud Specialist |"
url: "https://hashnode.com/@sumanprasad"
---

@sumanprasad

# Prasad Suman Mohan

@sumanprasad · India · Joined January 2023

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

[Website](https://linktr.ee/sumanprasad007)

### [Day 19: Kubeflow for MLOps - Architecture, Components & Lifecycle](https://prasadsuman.hashnode.dev/day-19-kubeflow-for-mlops-architecture-components-lifecycle?utm_source=hashnode&utm_medium=feed)

Introduction When we move from experimenting with ML models locally to deploying them in production, things get messy quickly — reproducibility issues, pipeline inconsistencies, manual workflows, an

5 min read · [prasadsuman.hashnode.dev](https://prasadsuman.hashnode.dev/day-19-kubeflow-for-mlops-architecture-components-lifecycle?utm_source=hashnode&utm_medium=feed)

[May 18](https://hashnode.com/posts/day-19-kubeflow-for-mlops-architecture-components-lifecycle/6a119ce21a3cf7bffc1affbc)

### [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

4 min read · [prasadsuman.hashnode.dev](https://prasadsuman.hashnode.dev/day-18-deploy-and-serve-model-for-inference-using-aws-sagemaker?utm_source=hashnode&utm_medium=feed)

[May 11](https://hashnode.com/posts/day-18-deploy-and-serve-model-for-inference-using-aws-sagemaker/6a016274786d9353ddcdb08d)

### [Day 17 - Create and Save Models to SageMaker](https://prasadsuman.hashnode.dev/day-17-create-and-save-models-to-sagemaker?utm_source=hashnode&utm_medium=feed)

From Training a Model to Storing It in S3 for Production Use In the previous sections, we: set up SageMaker infrastructure configured IAM roles and Studio understood how SageMaker simplifies MLOp

5 min read · [prasadsuman.hashnode.dev](https://prasadsuman.hashnode.dev/day-17-create-and-save-models-to-sagemaker?utm_source=hashnode&utm_medium=feed)

[May 4](https://hashnode.com/posts/day-17-create-and-save-models-to-sagemaker/69f827e5ee0ce64c266f024d)

### [Day 16: End-to-End Setup of SageMaker Using AWS CLI](https://prasadsuman.hashnode.dev/day-16-end-to-end-setup-of-sagemaker-using-aws-cli?utm_source=hashnode&utm_medium=feed)

Building a Secure, Production-Ready SageMaker Studio with ABAC In the previous section, we understood: What SageMaker is Why it exists What problems it solves Now, we move to the hands-on imple

7 min read · [prasadsuman.hashnode.dev](https://prasadsuman.hashnode.dev/day-16-end-to-end-setup-of-sagemaker-using-aws-cli?utm_source=hashnode&utm_medium=feed)

[Apr 27](https://hashnode.com/posts/day-16-end-to-end-setup-of-sagemaker-using-aws-cli/69eeed76d4a629a7ccbfccee)

### [Day 15 - SageMaker Fully managed AWS MLOps Tool](https://prasadsuman.hashnode.dev/day-15-sagemaker-fully-managed-aws-mlops-tool?utm_source=hashnode&utm_medium=feed)

Understanding Managed MLOps vs VM, Kubernetes, and KServe In the previous parts of this series, we explored multiple ways to deploy machine learning models: Virtual Machines (manual setup) Kubernete

6 min read · [prasadsuman.hashnode.dev](https://prasadsuman.hashnode.dev/day-15-sagemaker-fully-managed-aws-mlops-tool?utm_source=hashnode&utm_medium=feed)

[Apr 20](https://hashnode.com/posts/day-15-sagemaker-fully-managed-aws-mlops-tool/69e5b2ef77e9edf6a5a02987)