---
# System prepended metadata

title: Workshop Amazon SageMaker Unified Studio para YPF

---


# Workshop Amazon SageMaker Unified Studio para YPF


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

## Encuesta (CSAT): 

## Agenda Workshop

### Bloque AM
| Hora | Tema | Tipo | Duración | Speaker |
| :---: | :--- | :---: | :---: | :---: |
| 9:00 AM | Bienvenida y presentaciones || 15 min | Account Team |
| 9:15 AM | Intro a Data y ML en AWS:<br>Amazon SageMaker Unified Studio | Presentación | 30 min | Pablo Sierra |
| 9:45 AM | SageMaker Unified Studio: Set-up | Labs [1](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/01auth) y [2](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/02studio) | 30 min | Pablo Sierra |
| 10:15 AM | SageMaker: Desarrollo de modelos de ML | Presentación | 30 min | Rodrigo Alarcón |
| 10:45 AM | BREAK | | 15 min | |
| 11:00 AM | SageMaker AI: Entrenando y desplegando modelos de ML | [Lab 7.1.2](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/07aiml/01-ml/02-train-and-deploy) | 45 min | Rodrigo Alarcón |
| 11:45 AM | SageMaker AI: MLOps usando ML Pipelines | Presentación<br>y [Lab 7.1.3](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/07aiml/01-ml/03-pipelines) | 60 min | Rodrigo Alarcón |
| 12:45 PM | Discusión y Q&A |  | 15 min | Rodrigo Alarcón |
| 1:00 PM | **ALMUERZO** |  | 60 min | |

### Bloque PM
| Hora | Tema | Tipo | Duración | Speaker |
| :---: | :--- | :---: | :---: | :---: |
| 2:00 PM | Desarrollo de aplicaciones de IA generativa en SageMaker | Presentación | 30 min | Pablo Sierra |
| 2:30 PM | Bedrock en SageMaker Unified Studio: Construyendo un chatbot | Labs [8.1](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/08genai/01-explore) y [8.2](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/08genai/02-chatapps) | 45 min | Pablo Sierra |
| 3:15 PM | BREAK || 15 min | |
| 3:30 PM | (Opcional) SageMaker AI: Fine-tuning de LLM | Demo | 30 min | Rodrigo Alarcón |
| 4:00 PM | Discusión y Q&A || 30 min | Rodrigo Alarcón |
| 4:30 PM | Quiz, Cierre y Siguientes Pasos || 30 min | Account Team |


## Ingreso a la consola de AWS
* [One-click link para Workshop](https://catalog.us-east-1.prod.workshops.aws/join?access-code=6bc3-0878e9-da)
* [Lab Setup](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/01auth/02awsevent)

## Lab 0. Setup inicial. SageMaker Unified Studio
Secciones:
* [1. Getting Started](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/01auth/02awsevent)
* [2. Unified Studio](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/02studio)
    * En la sección **Create project**, crear un proyecto usando el caso de uso **All capabilities use case**, con el perfil **All capabilities**
    * La creación de los recursos del proyecto puede demorar entre 5-10 minutos. Mientras este se crea,  pueden avanzar hacia la sección **Project profiles, Blueprints and Amazon Q** para explorar las distintas componentes administrativas de SageMaker Unified Studio.

## Lab 1. SageMaker AI: Entrenando y desplegando modelos de ML
* Instrucciones: 
    * [Pre-requisitos](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/07aiml/01-ml/01-prerequisites)
        * Ya debería haber un **Space** en proceso de creación (**Starting**). Para ver su configuración y modificar el tamaño de instancia y su imagen, deberán esperar a que se encuentre en estado **Running**.
        * En paralelo, mientras se crea el **Space**, pueden avanzar creando el **MLFlow Tracking Server**.
        * La creación del **MLFlow Tracking Server** demora entre 15-20 minutos. No es necesario esperar a que termine de crearse para este Lab. En paralelo, pueden avanzar con el resto de los pasos de esta sección (Dataset, IAM Policy).
        * Para cargar el dataset, la opción a seleccionar es **Create table** (en vez de **Upload data**).
    * [Train and Deploy the ML model](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/07aiml/01-ml/02-train-and-deploy)
        * Validar que el **MLFlow Tracking Server** se encuentre desplegado (**On**) antes de ejecutar los pasos del notebook.
        * En **Data Preparation**, para la query SQL, substituir el nombre de la DB del catálogo (que comienza con `salesmarketing_`):
```sql
-- Copy/Paste your Project database name from cell above
-- select * from "awsdatacatalog"."YOUR PROJECT TABLE"."5000-sales-records"
select * from "awsdatacatalog"."<NOMBRE_DE_LA_DB>."5000-sales-records"
```

    * Para este workshop, trabajaremos con la opción ** 
* Servicios Clave: 
    * Amazon SageMaker Unified Studio
    * Amazon SageMaker AI: Processing, Training, Inference

## Lab 2. SageMaker AI: MLOps usando ML Pipelines
* Instrucciones:
    * [MLOps using Sagemaker ML Pipelines](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/07aiml/01-ml/03-pipelines)
* Servicios Clave: 
    * Amazon SageMaker Pipelines
    * Amazon SageMaker Model Registry
    * MLflow en SageMaker Unified Studio

## Lab 3. Bedrock en SageMaker Unified Studio: Construyendo un chatbot
* Instrucciones:
    * [Exploring generative AI models with Amazon Bedrock ](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/08genai/01-explore)
    * [Building generative AI Chat agent apps with Amazon Bedrock](https://catalog.us-east-1.prod.workshops.aws/workshops/06dbe60c-3a94-463e-8ac2-18c7f85788d4/en-US/08genai/02-chatapps) 
* Servicios Clave: 
    * Amazon Bedrock en SageMaker Unified Studio
    * Bedrock guardrails

## 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)
* [AWS Ramp-Up Guide: Generative AI](https://d1.awsstatic.com/onedam/marketing-channels/website/aws/en_US/training/approved/pdfs/ramp-up-guides/Ramp-Up_Guide_Generative_AI.pdf) (Training & Certification)
* [Learn AI/ML on AWS](https://aws.amazon.com/training/learn-about/machine-learning/) (AWS Skill Builder paths)
* [Learn Generative AI on AWS](https://aws.amazon.com/training/learn-about/generative-ai/) (AWS Skill Builder paths)
* [MLOps Engineering on AWS](https://aws.amazon.com/training/classroom/mlops-engineering-on-aws/) (Training & Certification)
* [Next-generation Amazon SageMaker: The center for data, analytics & AI](https://www.youtube.com/watch?v=5ZN-90fi3II) (AWS re:Invent 2024)
* [Amazon SageMaker Unified Studio User Guide](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html) (Guide)
* [Amazon SageMaker Examples](https://github.com/aws/amazon-sagemaker-examples)
* [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/index.html)
* [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)
* [Build consistent and portable ML environments with containers](https://www.youtube.com/watch?v=JTpYGq_N2Co) (re:Invent 2020)
* [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)
* [Amazon Q Developer Tiers](https://aws.amazon.com/q/developer/pricing/)
