Introduction to Amazon SageMaker Serverless Inference | Concepts & Code examples

Amazon SageMaker Serverless Inference is a model hosting feature that lets you deploy endpoints for inference that automatically starts and scales the compute resources based on traffic. With SageMaker Serverless Inference you don’t have to manage instance types and you only pay for prediction requests and time taken to process those requests. In this video we discuss key concepts, who is it for, how to use it and walkthrough a code example showing how to host a Serverless inference endpoint. Code examples: https://github.com/shashankprasanna/s... reinvent summary blog post: https://towardsdatascience.com/aws-re...

Join Today
Choose the right instance for inference deployment with SageMaker Inference Recommender
▶︎

Choose the right instance for inference deployment with SageMaker Inference Recommender

Reinforcement Learning | Build Your Own LLM Workshop #22
▶︎

Reinforcement Learning | Build Your Own LLM Workshop #22

Aprenda AWS Lambda neste Curso Prático GRATUITO! | Aula 17 - #70
▶︎

Aprenda AWS Lambda neste Curso Prático GRATUITO! | Aula 17 - #70

What is Amazon SageMaker?
▶︎

What is Amazon SageMaker?

YOUR CODE! AT SCALE! Amazon SageMaker Script Mode
▶︎

YOUR CODE! AT SCALE! Amazon SageMaker Script Mode

AWS Summit DC 2022 - Amazon SageMaker Inference explained: Which style is right for you?
▶︎

AWS Summit DC 2022 - Amazon SageMaker Inference explained: Which style is right for you?

SageMaker Inference Demystified: Choose the Best Deployment Option for Your ML Workload
▶︎

SageMaker Inference Demystified: Choose the Best Deployment Option for Your ML Workload

The Easiest Way to Build Machine Learning Models (AWS Sagemaker) MLOps
▶︎

The Easiest Way to Build Machine Learning Models (AWS Sagemaker) MLOps

Amazon Sagemaker Tutorial | AWS SageMaker Tutorial | How to Use Amazon SageMaker | Machine Learning
▶︎

Amazon Sagemaker Tutorial | AWS SageMaker Tutorial | How to Use Amazon SageMaker | Machine Learning

AWS Explained: The Most Important AWS Services To Know
▶︎

AWS Explained: The Most Important AWS Services To Know

ML Drift: Identifying Issues Before You Have a Problem
▶︎

ML Drift: Identifying Issues Before You Have a Problem

STEP BY STEP TO MACHINE LEARNING WITH SAGEMAKER (getting started with Amazon Sagemaker)
▶︎

STEP BY STEP TO MACHINE LEARNING WITH SAGEMAKER (getting started with Amazon Sagemaker)

AWS Sagemaker tutorial | Build and deploy a Machine Learning API with Python
▶︎

AWS Sagemaker tutorial | Build and deploy a Machine Learning API with Python

Deliver high-performance ML models faster with MLOps tools
▶︎

Deliver high-performance ML models faster with MLOps tools

AWS re:Invent 2022 - Deploy ML models for inference at high performance & low cost, ft AT&T (AIM302)
▶︎

AWS re:Invent 2022 - Deploy ML models for inference at high performance & low cost, ft AT&T (AIM302)

AWS re:Invent 2020: Implementing MLOps practices with Amazon SageMaker
▶︎

AWS re:Invent 2020: Implementing MLOps practices with Amazon SageMaker

Deploy Your ML Models to Production at Scale with Amazon SageMaker
▶︎

Deploy Your ML Models to Production at Scale with Amazon SageMaker

Amazon SageMaker Studio - A Fully Integrated Development Environment For Machine Learning
▶︎

Amazon SageMaker Studio - A Fully Integrated Development Environment For Machine Learning

Game On! - SageMaker STUDIO vs SageMaker NOTEBOOKS
▶︎

Game On! - SageMaker STUDIO vs SageMaker NOTEBOOKS

AWS VPC & Subnets | Amazon Web Services BASICS
▶︎

AWS VPC & Subnets | Amazon Web Services BASICS