Proyecto final de la asignatura de Arquitecturas Empresariales.
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Updated
May 18, 2022 - Jupyter Notebook
Proyecto final de la asignatura de Arquitecturas Empresariales.
Training different models for Predicting Bike Sharing Demand by using AutoGluon's TabularPredictor.fit() on AWS SageMaker Studio
A repo for creating Sagemaker jobs
The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.
Terraform module to create and manage a SageMaker studio
This repository contains samples for fine-tuning embedding models using Amazon SageMaker. Embedding models are useful for tasks such as semantic similarity, text clustering, and information retrieval. Fine-tuning these models on your specific domain data can greatly improve their performance.
A small collection of custom kernels for running Sagemaker Notebooks an Training Jobs
A Python-based library for automating the migration of EFS storage from one SageMaker Studio domain to another using AWS DataSync
The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.
Time Series Analysis on Sagemaker
Data governance through AWS LakeFormation credentials vending API
Best practices for prompting for Meta's Llama2 Large Language Model using Amazon Sagemaker
A search application using Aurora Postgresql and pgvector for an online retail store product catalog
Workshop for running HuggingFace Models on Amazon SageMaker.
Amazon SageMaker training jobs using Snowpark Python API
Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.
This solution shows how to deliver reusable and self-contained custom components to Amazon SageMaker environment using AWS Service Catalog, AWS CloudFormation, SageMaker Projects and SageMaker Pipelines.
This is an example to demonstrate Amazon SageMaker Data Wrangler capabilities. The workshop showcases entire ML workflow steps for Diabetic Patient Readmission Dataset from UCI.
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