dmatrix Goto Github PK

academy

Ray tutorials from Anyscale

ad-serving-tutorial

Tecton's tutorial for using an Enterprise Feature Store.

cookbook

Jules Fork for PRs

couchbase-java-client

The official Java client for Couchbase Server

couchbase-python-client

Couchbase Python client library (official)

data-assets

Assets for blogs, datasets, slides, notebooks etc

delta

An open-source storage layer that brings scalable, ACID transactions to Apache Spark™ and big data workloads.

devaiworld23

Tutorial for Ray Core Workshop for Dev AI World 2023

ds4g-workshop

examples

These are some code examples

examples-1

Example code on running Confluent Platform

feast

Feature Store for Machine Learning

feast-driver-ranking-tutorial

feast-fraud-tutorial

Resources backing the Feast fraud tutorial on GCP

feast_workshops

A series of workshop modules introducing Feast feature store.

genai-cookbook

A mixture of Gen AI cookbook recipes for Gen AI applications.

go

PubNub clients for Go

go-github

Go library for accessing the GitHub API

google-colab

A general repo for creating Colab Jupyter notebooks for MLflow tests and issues

incubator-mynewt-site

Mirror of Apache MyNewt site (Incubating)

influxdb

Scalable datastore for metrics, events, and real-time analytics

influxdb-java

Java client for InfluxDB

influxdb-python

Python client for InfluxDB

jsd-mlflow-examples

This is a collection of MLflow examples that you can directly run with mlflow command

koalas

Koalas: pandas API on Apache Spark

learningsparkv2

This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]

misc-code

A repo for example code for technologies and platforms: MLflow, PyTorch, Python Language features, Spark and Scala, etc.

mlflow

Open source platform for the machine learning lifecycle

mlflow-example

An MLflow Executable Example

mlflow-workshop-part-1

Partly lecture and partly a hands-on tutorial and workshop, this is a three part series on how to get started with MLflow. In this three part series, we will cover MLflow Tracking, Projects, Models, and Model Registry.