MLOS is a Data Science powered infrastructure and methodology to democratize and automate Performance Engineering. MLOS enables continuous, instance-based, robust, and trackable systems optimization.
Обновлено 2024-09-18 03:45:43 +03:00
Release notes and system requirements for our various Firefoxen
Обновлено 2024-09-17 23:50:57 +03:00
Jira Bugzilla Integration (JBI) - system to sync bugs and issues
Обновлено 2024-09-17 17:05:11 +03:00
A validation and profiling tool for AI infrastructure
Обновлено 2024-09-17 16:58:47 +03:00
Best Practices on Recommendation Systems
azure
microsoft
machine-learning
python
deep-learning
kubernetes
data-science
artificial-intelligence
jupyter-notebook
tutorial
operationalization
ranking
rating
recommendation
recommendation-algorithm
recommendation-engine
recommendation-system
recommender
Обновлено 2024-09-17 05:40:34 +03:00
Python library for interacting with the Firefox Accounts ecosystem
Обновлено 2024-09-09 20:35:03 +03:00
This package features data-science related tasks for developing new recognizers for Presidio. It is used for the evaluation of the entire system, as well as for evaluating specific PII recognizers or PII detection models.
machine-learning
deep-learning
nlp
natural-language-processing
privacy
ner
transformers
pii
named-entity-recognition
spacy
flair
Обновлено 2024-08-07 19:17:23 +03:00
Microsoft Azure Data Lake Store Filesystem Library for Python
Обновлено 2024-08-01 22:48:50 +03:00
Variational inference for hierarchical dynamical systems
Обновлено 2024-07-25 14:00:08 +03:00
System for AI Education Resource.
Обновлено 2024-06-21 06:00:34 +03:00
the grand unified configuration system
Обновлено 2024-03-29 22:52:17 +03:00
Remote Assets of Firefox Messaging System
Обновлено 2023-11-02 00:58:40 +03:00
Intelligent Conversation Engine: Code and Pre-trained Systems. Version 0.2.0.
Обновлено 2023-07-22 11:03:07 +03:00
Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.
Обновлено 2023-06-12 21:21:58 +03:00
A command line tool to help with shipping system addons in Balrog
Обновлено 2023-02-22 13:07:19 +03:00
AI Assistant for Building Reliable, High-performing and Fair Multilingual NLP Systems
Обновлено 2022-08-19 10:21:28 +03:00
The official Sublime Text package for Go build system integration.
Обновлено 2022-07-22 18:52:51 +03:00
Qlib-Server is the data server system for Qlib. It enable Qlib to run in online mode. Under online mode, the data will be deployed as a shared data service. The data and their cache will be shared by all the clients. The data retrieval performance is expected to be improved due to a higher rate of cache hits. It will consume less disk space, too.
Обновлено 2022-07-08 05:15:09 +03:00
An example project which contains the Unity components necessary to complete Navigation2's SLAM tutorial with a Turtlebot3, using a custom Unity environment in place of Gazebo.
Обновлено 2021-12-13 23:16:54 +03:00
Build & packaging system, responsible for the Mono project distribution for Mac
Обновлено 2021-09-28 14:49:20 +03:00
The Redash dashboard utilities for Firefox Messaging System
Обновлено 2021-06-01 23:12:10 +03:00
Обновлено 2021-03-30 00:08:22 +03:00
Some demo code from a Python brownbag given to the Linux Systems Group
Обновлено 2020-11-13 04:15:45 +03:00
Backup tool for VM file systems to Azure Blob Storage.
Обновлено 2020-10-28 00:31:00 +03:00
The client program for ADX team's automation system
Обновлено 2020-10-28 00:21:52 +03:00
A tool which can automatically collect all the system diagnosis information for a Hyper-V Linux guest.
Обновлено 2020-09-04 00:37:09 +03:00
Peregrine is a workload optimization platform for cloud query engines. The goal of Peregrine is three-fold: 1. make it easier to ingest and analyze query workload telemetry into a common engine-agnostic representation, 2. help developers to quickly build workload optimization applications to reduce overall costs and improve operational efficiency, and 3. providing better experience to the customers in the form of workload insights, actionable recommendations, and self-tuning capabilities.
Обновлено 2020-08-31 07:53:14 +03:00
"Solving problems with Deep Learning: an in-depth example using PyTorch and its ecosystem" tutorial/lab
Обновлено 2020-06-02 14:25:20 +03:00