How to Build A Complete ML Architecture With SageMaker + API Gateway + Lambda | PART1
This video is the first installment in a comprehensive two-part series that guides viewers through the entire process of developing and deploying a machine learning model using Amazon Web Services (AWS) tools.
The series covers the journey from fine-tuning a machine learning model to its deployment in a production environment, utilizing AWS SageMaker for model training and hosting. Additionally, it demonstrates how to create a serverless API using AWS API Gateway and Lambda functions to interact with the deployed model endpoints, enabling practical applications of the trained model.
PART 2 is available below:
► How to Build A Complete ML Architecture With SageMaker + API Gateway + Lambda | PART2: • How to Build A Complete ML Architectu...
CHAPTERS
0:00 - Introduction and Goal
01:39 - Prerequisites and Setup
06:40 - Configure S3 and SageMaker
11:14 - Model Fine-tuning with SageMaker
22:11 - Deploy Model to SageMaker Endpoint
24:05 - Model Inferencing & Performance
25:35 - Next Steps
Source code:
https://github.com/keitazoumana/AWS-S...
►LLM Projects:
How to Build Anything With AI Agents - With Code: • How to Build Anything With AI Agents ...
PDF to JSON: LLM-Powered Data Extraction In Python: • PDF to JSON: LLM-Powered Data Extract...
Multimodal RAG: Text, Images, Tables & Audio Pipeline: • Multimodal RAG: Text, Images, Tables ...
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