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# System prepended metadata

title: Workshop Amazon SageMaker para Grupo AJE

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# Workshop Amazon SageMaker para Grupo AJE


Bienvenidos al workshop de Amazon SageMaker, el cual les ayudará a entender de forma práctica cómo implementar un pipeline de Machine Learning en AWS.

## Encuesta (CSAT): 

## Agenda Workshop

| Hora | Tema | Recursos/Enlaces |
| ------ | ----------- | ----------- |
09:00 - 10:00|Introduction to Amazon SageMaker|
10:00 - 10:20|Lab 1. SageMaker Studio Notebooks & Feature Engineering| [Laboratorio](https://catalog.us-east-1.prod.workshops.aws/workshops/63069e26-921c-4ce1-9cc7-dd882ff62575/en-US/lab1/option2)
10:20 - 10:45|Lab 2. Train, Tune and Deploy model using SageMaker Built-in Algorithm| [Laboratorio](https://catalog.us-east-1.prod.workshops.aws/workshops/63069e26-921c-4ce1-9cc7-dd882ff62575/en-US/lab2)
10:45 - 11:00|Break|
11:00 - 11:30|Create Model, Prediction and Inference|
11:30 - 13:00|Lab 3. SageMaker Pipelines| [Laboratorio](https://catalog.us-east-1.prod.workshops.aws/workshops/63069e26-921c-4ce1-9cc7-dd882ff62575/en-US/lab6)



## Ingreso a la consola de AWS

* [Lab Setup](https://catalog.us-east-1.prod.workshops.aws/join?access-code=bde4-063714-69](https://catalog.us-east-1.prod.workshops.aws/join?access-code=bde4-063714-69))

## Lab 1. SageMaker Studio Notebooks & Feature Engineering
Servicios Clave: 
* Amazon SageMaker Studio

Trabajaremos con la **Opción 2: Numpy and Pandas**

## Lab 2. Train, Tune and Deploy model using SageMaker Built-in Algorithm
Servicios Clave: 
* Amazon SageMaker Training
* Amazon SageMaker Hosting
* Amazon SageMaker AutoTuning (HPO)

## Lab 3. SageMaker Pipelines
Servicios Clave: 
* Amazon SageMaker Pipelines
* Amazon SageMaker Model Registry

## Material complementario

Los siguientes recursos les ayudarán a profundizar en los temas revisados durante el workshop:

* [AWS Ramp-Up Guide: Machine Learning](https://d1.awsstatic.com/training-and-certification/ramp-up_guides/Ramp-Up_Guide_Machine_Learning.pdf) (Training & Certification)
* [MLOps Engineering on AWS](https://aws.amazon.com/training/classroom/mlops-engineering-on-aws/) (Training & Certification)
* [Amazon SageMaker Developer Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) (Guide)
* [MLOps con SageMaker Pipelines y SageMaker Projects](http://amazon-sagemaker.com/es/mlops/) (Workshop)
* [Roadmap para una base empresarial de MLOps con Amazon SageMaker](https://aws.amazon.com/es/blogs/aws-spanish/roadmap-para-una-base-empresarial-de-mlops-con-amazon-sagemaker/) (AWS Blogs)
* [AWS MLOps Framework](https://aws.amazon.com/solutions/implementations/aws-mlops-framework/) (AWS Solutions Implementation)
* [Build consistent and portable ML environments with containers](https://www.youtube.com/watch?v=JTpYGq_N2Co) (re:Invent 2020)
* [Amazon SageMaker Example Notebooks](https://sagemaker-examples.readthedocs.io/en/latest/)
* [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/index.html)
* [Configuring Amazon SageMaker Studio for teams and groups with complete resource isolation](https://aws.amazon.com/blogs/machine-learning/configuring-amazon-sagemaker-studio-for-teams-and-groups-with-complete-resource-isolation/) (AWS Blogs)
* [Introducing the Amazon SageMaker Serverless Inference Benchmarking Toolkit](https://aws.amazon.com/blogs/machine-learning/introducing-the-amazon-sagemaker-serverless-inference-benchmarking-toolkit/) (AWS Blogs)