automl

Anomaly (fraud) detection pipeline on credit card transaction data using Isolation Forest machine learning model and Kedro framework Link

UnionML is an open source MLOps framework that reduces the boilerplate, complexity, and friction that comes with building models and deploying them to production. Taking inspiration from web protocols, Link

Ray AI Runtime (AIR) is an open-source toolkit for building ML applications. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more. Link

A model registry is a central repository that is used to version control Machine Learning (ML) models. It simply tracks the models while they move between training, production, monitoring, and deployment. Link

This document is intended to help those with a basic knowledge of machine learning get the benefit of Google’s best practices in machine learning. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machine­-learned model, then you have the necessary background to read this document.

An easy-to-follow comprehensive guide on using a stack of powerful tools to train and serve an AutoML pipeline for insurance cross-sell Link

Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost the productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. Metaflow provides a unified API to the infrastructure stack that is required to execute data science projects, from prototype to production. Link

BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports Multiple ML frameworks, including Tensorflow, PyTorch, Keras, XGBoost and more Cloud native deployment with Docker, Kubernetes, AWS, Azure and many more High-Performance online API serving and offline batch serving Web dashboards and APIs for model registry and deployment management Link

Write ML Pipelines that will make your MLOps team happy: follow a clean separation of responsibility between model code and ops code. This article show you how to do that. Link

EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. Key Functionality Automation - Makes machine learning easier. Avoid training and tuning models by hand. Includes data quality checks, cross-validation and more. Data Checks - Catches and warns of problems with your data and problem setup before modeling. End-to-end - Constructs and optimizes pipelines that include state-of-the-art preprocessing, feature engineering, feature selection, and a variety of modeling techniques.