awesome-ai-safety
materialize
awesome-ai-safety | materialize | |
---|---|---|
5 | 120 | |
140 | 5,608 | |
9.3% | 1.0% | |
5.6 | 10.0 | |
7 months ago | about 4 hours ago | |
Rust | ||
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-ai-safety
-
Ask HN: Who is hiring? (October 2023)
Giskard - Testing framework for ML models| Multiple roles | Full-time | France | https://giskard.ai/
We are building the first collaborative & open-source Quality Assurance platform for all ML models - including Large Language Models.
Founded in 2021 in Paris by ex-Dataiku engineers, we are an emerging player in the fast-growing market of AI Quality & Safety.
Giskard helps Data Scientists & ML Engineering teams collaborate to evaluate, test & monitor AI models. We help organizations increase the efficiency of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models. Our open-source platform is used by dozens of ML teams across industries, both at enterprise companies & startups.
In 2022, we raised our first round of 1.5 million euros, led by Elaia, with participation from Bessemer and notable angel investors including the CTO of Hugging Face. To read more about this fundraising and how it will accelerate our growth, you can read this announcement: https://www.giskard.ai/knowledge/news-fundraising-2022
In 2023, we received a strategic investment from the European Commission to build a SaaS platform to automate compliance with the upcoming EU AI regulation. You can read more here: https://www.giskard.ai/knowledge/1-000-github-stars-3meu-and...
We are assembling a team of champions: Software Engineers, Machine Learning researchers, and Data Scientists ; to build our AI Quality platform and expand it to new types of AI models and industries. We have a culture of continuous learning & quality, and we help each other achieve high standards & goals!
We aim to grow from 15 to 25 people in the next 12 months. We're hiring the following roles:
-
Ask HN: Who is hiring? (August 2023)
Giskard - Testing framework for ML models| Multiple roles | Full-time | France | https://giskard.ai/
We are building the first collaborative & open-source Quality Assurance platform for all ML models - including Large Language Models.
Founded in 2021 in Paris by ex-Dataiku engineers, we are an emerging player in the fast-growing market of AI Safety & Security.
Giskard helps Data Scientists & ML Engineering teams collaborate to evaluate, test & monitor AI models. We help organizations increase the efficiency of their AI development workflow, eliminate risks of AI biases and ensure robust, reliable & ethical AI models. Our open-source platform is used by dozens of ML teams across industries, both at enterprise companies & startups.
In 2022, we raised our first round of 1.5 million euros, led by Elaia, with participation from Bessemer and notable angel investors including the CTO of Hugging Face. To read more about this fundraising and how it will accelerate our growth, you can read this announcement: https://www.giskard.ai/knowledge/news-fundraising-2022
In 2023, we received a strategic investment from the European Commission to build a SaaS platform to automate compliance with the upcoming EU AI regulation. You can read more here: https://www.giskard.ai/knowledge/1-000-github-stars-3meu-and...
We are assembling a team of champions: Software Engineers, Machine Learning researchers, and Data Scientists ; to build our AI Quality platform and expand it to new types of AI models and industries. We have a culture of continuous learning & quality, and we help each other achieve high standards & goals!
We aim to grow from 15 to 25 people in the next 12 months. We're hiring the following roles:
* Software Engineer - https://apply.workable.com/giskard/j/AD2C90B581/ (Python, Java, Typescript, Vue.js, Cloud skills)
* Machine Learning Researcher - https://apply.workable.com/giskard/j/E89FE8E310/ (post-PhD)
* Data Science lead - https://apply.workable.com/giskard/j/E89FE8E310/ (ML + consulting experience required)
* Growth marketing intern - https://apply.workable.com/giskard/j/C8635E9B0C/
* Data Science intern - https://apply.workable.com/giskard/j/7F0B341852/
-
Show HN: Python library to scan ML models for vulnerabilities
Hi! Iāve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, ā¦) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production.
It is still a beta and I would love to hear your feedback if you have the time to try it out.
We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started.
If you are interested in the code:
https://github.com/Giskard-AI/giskard/tree/main/python-clien...
-
[R] Awesome AI Safety ā A curated list of papers & technical articles on AI Quality & Safety
Repository: https://github.com/Giskard-AI/awesome-ai-safety
- AI Safety ā curated papers for safer, ethical, and reliable AI
materialize
-
Ask HN: How Can I Make My Front End React to Database Changes in Real-Time?
[2] https://materialize.com/
-
Choosing Between a Streaming Database and a Stream Processing Framework in Python
To fully leverage the data is the new oil concept, companies require a special database designed to manage vast amounts of data instantly. This need has led to different database forms, including NoSQL databases, vector databases, time-series databases, graph databases, in-memory databases, and in-memory data grids. Recent years have seen the rise of cloud-based streaming databases such as RisingWave, Materialize, DeltaStream, and TimePlus. While they each have distinct commercial and technical approaches, their overarching goal remains consistent: to offer users cloud-based streaming database services.
-
Proton, a fast and lightweight alternative to Apache Flink
> Materialize no longer provide the latest code as an open-source software that you can download and try. It turned from a single binary design to cloud-only micro-service
Materialize CTO here. Just wanted to clarify that Materialize has always been source available, not OSS. Since our initial release in 2020, we've been licensed under the Business Source License (BSL), like MariaDB and CockroachDB. Under the BSL, each release does eventually transition to Apache 2.0, four years after its initial release.
Our core codebase is absolutely still publicly available on GitHub [0], and our developer guide for building and running Materialize on your own machine is still public [1].
It is true that we substantially rearchitected Materialize in 2022 to be more "cloud-native". Our new cloud offering offers horizontal scalability and fault toleranceāour two most requested features in the single-binary days. I wouldn't call the new architecture a microservices design though! There are only 2-3 services, each quite substantial, in the new architecture (loosely: a compute service, an orchestration service, and, soon, a load balancing service).
We do push folks to sign up for a free trial of our hosted cloud offering [2] these days, rather than trying to start off by running things locally, as we generally want folks' first impression of Materialize to be of the version that we support for production use cases. A all-in-one single machine Docker image does still exist, if you know where to look, but it's very much use-at-your-own-risk, and we don't recommend using it for anything serious, but it's there to support e.g. academic work that wants to evaluate Materialize's capabilities to incrementally maintain recursive SQL queries.
If folks have questions about Materialize, we've got a lively community Slack [3] where you can connect directly with our product and engineering teams.
[0]: https://github.com/MaterializeInc/materialize/tree/main
- What I Talk About When I Talk About Query Optimizer (Part 1): IR Design
-
We Built a Streaming SQL Engine
Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.
https://github.com/timelydataflow/differential-dataflow
https://materialize.com/
-
Ask HN: Who is hiring? (October 2023)
Materialize | Full-Time | NYC Office or Remote | https://materialize.com
Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on whatās happening right now. Keep the familiar SQL, keep the proven architecture of cloud warehouses but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.
Materialize is the operational data warehouse built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable ā all in a familiar SQL UI.
Senior/Staff Product Manager - https://grnh.se/69754ebf4us
Senior Frontend Engineer - https://grnh.se/7010bdb64us
===
Investors include Redpoint, Lightspeed and Kleiner Perkins.
-
Ask HN: Who is hiring? (June 2023)
Materialize | EM (Compute), Senior PM | New York, New York | https://materialize.com/
You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.
That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable ā all in a familiar SQL UI.
Engineering Manager, Compute - https://grnh.se/4e14099f4us
Senior Product Manager - https://grnh.se/587c36804us
VP of Marketing - https://grnh.se/9caac4b04us
- What are your favorite tools or components in the Kafka ecosystem?
- Ask HN: Who is hiring? (May 2023)
-
Dozer: A scalable Real-Time Data APIs backend written in Rust
How does it compare to https://materialize.com/ ?
What are some alternatives?
opentofu - OpenTofu lets you declaratively manage your cloud infrastructure.
ClickHouse - ClickHouseĀ® is a free analytics DBMS for big data
tabby - Self-hosted AI coding assistant
risingwave - SQL stream processing, analytics, and management. We decouple storage and compute to offer instant failover, dynamic scaling, speedy bootstrapping, and efficient joins.
awesome-langchain - š Awesome list of tools and projects with the awesome LangChain framework
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
giskard - š¢ Open-Source Evaluation & Testing for LLMs and ML models
rust-kafka-101 - Getting started with Rust and Kafka
refact - WebUI for Fine-Tuning and Self-hosting of Open-Source Large Language Models for Coding
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
nl-wallet - NL Public Reference Wallet
scryer-prolog - A modern Prolog implementation written mostly in Rust.