Edgar Bahilo Rodríguez's starred repositories
aws-serverless-workshops
Code and walkthrough labs to set up serverless applications for Wild Rydes workshops
feast-workshop
A workshop with several modules to help learn Feast, an open-source feature store
sfguide-recommender-pipeline
Snowflake Guide: Building a Recommendation Engine Using Snowflake & Amazon SageMaker
EngineeringMLOps
Engineering MLOps, published by Packt
pytest-adf
Pytest plugin for writing Azure Data Factory Integration Tests
modern-data-warehouse-dataops
DataOps for the Modern Data Warehouse on Microsoft Azure. https://aka.ms/mdw-dataops.
data-model-drift
Managing Data and Model Drift with Azure Machine Learning
FinRL-Meta
FinRL-Meta: Dynamic datasets and market environments for FinRL.
multilevel_modeling
Tutorial on multilevel modeling, using Gelman radon example
amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
neural_additive_models
stand alone Neural Additive Models, forked from google-reasearch for easy import to colab
MLOpsPython
MLOps using Azure ML Services and Azure DevOps
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
nixtla
TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.