MatheusDemoner

MatheusDemoner's Stars

• visenger/awesome-mlops

  A curated list of references for MLOps

  12.6k1.9k

• SeldonIO/MLServer

  An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more

  Language:Python717182

• jczic/MicroWebSrv

  A micro HTTP Web server that supports WebSockets, html/python language templating and routing handlers, for MicroPython (used on Pycom modules & ESP32)

  Language:Python652116

• daijro/hrequests

  🚀 Web scraping for humans

  Language:Python68543

• flet-dev/flet

  Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.

  Language:Python11.4k445

• box-key/pydata-kubernetes-mlflow

  A repo for demo at PyData NYC 2022

  Language:Python122

• mage-ai/mage-ai

  🧙 Build, run, and manage data pipelines for integrating and transforming data.

  Language:Python7.9k766

• Bren0Miranda/data-engineering-on-kubenetes

  Data Engineering applications on Kubernetes

  1

• apache/superset

  Apache Superset is a Data Visualization and Data Exploration Platform

  Language:TypeScript62.7k13.8k

• apache/druid

  Apache Druid: a high performance real-time analytics database.

  Language:Java13.5k3.7k

• lfreneda/statusinvest

  :money_with_wings: statusinvest.com.br stocks info web scraper

  Language:JavaScript6614

• codigoquant/python_para_investimentos

  Aplicações de Python para Finanças e Investimentos

  Language:Jupyter Notebook345142

• rlabbe/Kalman-and-Bayesian-Filters-in-Python

  Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

  Language:Jupyter Notebook16.7k4.2k

• faridrashidi/kaggle-solutions

  🏅 Collection of Kaggle Solutions and Ideas 🏅

  Language:HTML5k1.8k

• py-why/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.

  Language:Jupyter Notebook3.8k716

• letsdata/data-science-em-projetos

  Códigos do evento Data Science em Projetos

  Language:Jupyter Notebook168

• feature-engine/feature_engine

  Feature engineering package with sklearn like functionality

  Language:Python1.9k312