DXcarlos

DXcarlos's Stars

• databricks/mlops-stacks

  This repo provides a customizable stack for starting new ML projects on Databricks that follow production best-practices out of the box.

  Language:Python458156

• alirezadir/Machine-Learning-Interviews

  This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

  Language:Jupyter Notebook4.7k830

• bytedance/monolith

  ByteDance's Recommendation System

  Language:Python877125

• serodriguez68/designing-ml-systems-summary

  A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.

  9821

• serodriguez68/clean-architecture

  A detailed summary of Clean Architecture by Robert C Martin (Uncle Bob)

  29065

• timesler/facenet-pytorch

  Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models

  Language:Python4.5k951

• Galileo-Galilei/kedro-mlflow

  A kedro-plugin for integration of mlflow capabilities inside kedro projects (especially machine learning model versioning and packaging)

  Language:Python20334

• ipazc/mtcnn

  MTCNN face detection implementation for TensorFlow, as a PIP package.

  Language:Jupyter Notebook2.2k527

• balajisrinivas/Face-Mask-Detection

  Detecting face masks using Python, Keras, OpenCV on real video streams

  Language:Python485684

• prajnasb/observations

  Language:Jupyter Notebook433491

• NVIDIA/framework-reproducibility

  Providing reproducibility in deep learning frameworks

  Language:Python42440

• iterative/dvc

  🦉 Data Versioning and ML Experiments

  Language:Python13.9k1.2k

• MAIF/shapash

  🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

  Language:Jupyter Notebook2.7k334

• iterative/shtab

  ↔️ Automagic shell tab completion for Python CLI applications

  Language:Python37534

• klbostee/dumbo

  Python module that allows one to easily write and run Hadoop programs.

  Language:Python1k146

• allegroai/clearml

  ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

  Language:Python5.7k651

• marcotcr/checklist

  Beyond Accuracy: Behavioral Testing of NLP models with CheckList

  Language:Jupyter Notebook2k204

• amundsen-io/amundsen

  Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

  Language:Python4.4k959

• zenml-io/zenml

  ZenML 🙏: The bridge between ML and Ops. https://zenml.io.

  Language:Python4k436

• peopledoc/mlvtools-tutorial

  Tutorial for a new versioning Machine Learning pipeline

  Language:JavaScript8112

• microsoft/MLOps

  MLOps examples

  Language:Jupyter Notebook1.8k540

• deezer/spleeter

  Deezer source separation library including pretrained models.

  Language:Python25.9k2.8k

• MaartenGr/BERTopic

  Leveraging BERT and c-TF-IDF to create easily interpretable topics.

  Language:Python6.1k763

• iterative/cml

  ♾️ CML - Continuous Machine Learning | CI/CD for ML

  Language:JavaScript4k341

• rapidsai/cuml

  cuML - RAPIDS Machine Learning Library

  Language:C++4.2k530

• kLabUM/rrcf

  🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

  Language:Python496112

• explainX/explainx

  Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

  Language:Jupyter Notebook41554

• smazzanti/tds_black_box_models_more_explainable

  Jupyter Notebook used for writing the article "Black-Box models are actually more explainable than a Logistic Regression" published in Towards Data Science: https://towardsdatascience.com/black-box-models-are-actually-more-explainable-than-a-logistic-regression-f263c22795d

  Language:Jupyter Notebook7242

• AlexIoannides/pyspark-example-project

  Implementing best practices for PySpark ETL jobs and applications.

  Language:Python1.7k709

• ddangelov/Top2Vec

  Top2Vec learns jointly embedded topic, document and word vectors.

  Language:Python2.9k374