You will get AWS Sagemaker Model Training and Inference Deployment
Project details
I have created and deployed many Deep Learning ML models on AWS and Sageamaker services using various AWS and Sagemaker services like Sagemaker Training to train the models. On many projects, I completed the training, deployment, and monitoring of ML models and also created custom models for use cases like Object Detection, Object Tracking, and Image Segmentation using the Sagemaker Training.
I've used Sagemaker Inference to build a Batch/Realtime Inference, save the model in Model Registry, and also monitor the deployed models. These projects also involved Model development on Sagemaker and continuous Deployment of the Model with monitoring of the models. The Model prediction consisted of a real-time API where the prediction data was saved in S3 or Database for further evaluation.
You can check out my work on Upwork using AWS Sagemaker and ML pipelines while also leveraging many AWS services. I got a 5-star rating for all projects and I assure you that you will also get professional service with the end-to-end working of ML data pipelines.
I've used Sagemaker Inference to build a Batch/Realtime Inference, save the model in Model Registry, and also monitor the deployed models. These projects also involved Model development on Sagemaker and continuous Deployment of the Model with monitoring of the models. The Model prediction consisted of a real-time API where the prediction data was saved in S3 or Database for further evaluation.
You can check out my work on Upwork using AWS Sagemaker and ML pipelines while also leveraging many AWS services. I got a 5-star rating for all projects and I assure you that you will also get professional service with the end-to-end working of ML data pipelines.
What's included
| Service Tiers |
Starter
$3,000
|
Standard
$4,000
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 21 days | 30 days | 45 days |
Number of Revisions | 0 | 0 | 0 |
Number of Model Variations | 0 | 0 | 0 |
Number of Scenarios | 0 | 0 | 0 |
Number of Graphs/Charts | 0 | 0 | 0 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
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Pratik is a professional with excellent skills. He impressed us with his accurate thinking and timely judgment. Throughout the project, he communicated actively with us, provided precise solutions, and took responsibility for his duty, even going beyond it. If anyone is looking for a machine learning developer, I highly recommend Pratik. He is someone you can always trust!
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About Pratik
LLM, MCP, RAG - AI Agents Engineer | AI Speech | AI Vision | AWS / GCP
84%
Job Success
Barrie, Canada - 12:45 am local time
Professional experience in leveraging LLM and Vision AI agents to help businesses transform.
- Natural Language Understanding and LLM Models for Conversational Agents
- Speech Models for processing audio (Voice, Music, Speech-to-Text, Text-to-Speech)
- Computer Vision - Detection / Recognition / Tracking of objects in Video
- Cloud Technologies - GCP / AWS / Azure Services for deploying AI services in production
Professional Certifications
------------
- Microsoft Certified: Azure Fundamentals
- Microsoft Certified: Azure AI Fundamentals
- Google Certified Professional Data Engineer
- Google Certified Professional Cloud Architect
AWS Services
------------
AWS Sagemaker, AWS Redshift, AWS Lambda, AWS ElasticSearch, AWS Elastic Beanstalk, AWS EC2
GCP Skills
------------
Vertex AI Platform, BigQuery, AutoML, Cloud Function, Cloud Composer, Dataproc, Compute Engine
Education
------------
Post Graduate Certificate - Artificial Intelligence
Post Graduate Certificate - Big Data Analytics
Bachelor of Engineering (Information Technology)
Steps for completing your project
After purchasing the project, send requirements so Pratik can start the project.
Delivery time starts when Pratik receives requirements from you.
Pratik works on your project following the steps below.
Revisions may occur after the delivery date.
Discussion on various aspects of the project
Connecting for understanding the use case and deciding the feasibility of the development. Then can plan the project and share the Roadmap with milestones/stages. Then you can provide access to AWS resources, any existing code, files, etc.
Development of the Sagemaker Model training pipelines