joelschw

No this is a composability surface, Flyte is their workflow engine for durable execution etc

PhysicsX | Principal AI Engineer | London / NY / Remote | Full time | physicsx.ai

We're building an AI-native platform that turns physics simulations from days-long jobs into millisecond inference. Think: training deep learning surrogates on CFD, FEA, thermal data, and Large Physics Model (LPM) frontier research so engineers can explore thousands of design variants in a single session — across aerospace, semiconductors, energy, and automotive.

You should have the scar tissue of running agents in production and strong opinions on what the future looks like.

We're ~170 people, raised $135M Series B, and backed by General Catalyst, Atomico, Siemens, and Applied Materials. The team includes people from F1, SpaceX, Google, Palantir, and Tesla.

If this sounds interesting: https://job-boards.eu.greenhouse.io/physicsx/jobs/4804769101

Or email me directly: joel.schwarzmann [at] physicsx [dot] ai

You could also use braintrust here https://github.com/braintrustdata/braintrust-claude-plugin

January 6th was so egregious, I'm really not sure why they felt the need to exaggerate it.

Huge fan of Ibis, the value isn't that you can now use DuckDB... it's that your syntax will work when the next cool thing arrives too

Or you use Ibis and switch between the two at will!

I'd put this in the readme!

Big fan of evidence, really elegant design. There is also space for both declarative and imperative approaches when it comes to dashboarding / reporting etc.

The native GitHub feature in preview will make this a lot better for those able to use it https://github.blog/changelog/2023-03-01-feature-preview-ric...

Whilst I hate it - Jinja is so much more accessible for SQL coders.

I'm not sure it's framed like that, it's specifically talking about production patterns

If git did it...

Some similar concepts, but Kedro is specialised on the ML engineering workflow and team collaboration

Here you go - https://kedro.readthedocs.io/en/0.17.6/09_development/03_com...

Will point to some experiences open source users have had on this journey

https://medium.com/hacking-talent/production-code-for-data-s...

https://medium.com/google-cloud/migrate-kedro-pipeline-on-ve...

Some users have reported the Targets library is similar for that ecosystem

Kedro sort of fits into a niche where it just overlaps somewhat with 'orchestrators' like Prefect, Metaflow, Dagster, Airflow and others. What makes it slightly different is that it it is focused on the rapid development journey to production, providing guardrails for teams to co-develop ML projects in a way that nudges software engineering best practice and clean code.

The 'finished article' in many cases should be deployed in production in one of those tools which provide specialised bells and whistles like scheduling, monitoring and observability.

Regarding MLFlow, there is also a slight overlap in terms of experimentation, but not things like model serving. Kedro has a mechanism to track experiments, but it's more designed to give users with zero infrastructure something for free out of the box. It's been built in a way that it can be repurposed for more dedicated experiment tracking tools - the folks at neptune.ai built their own plug-in for this purpose: https://docs.neptune.ai/integrations-and-supported-tools/aut...

I should clarify by short term - the foreseeable future is a more appropriate description

Hello maintainer here - hopefully some of our open source users chime in, but my favourite will always be NASA: https://github.com/nasa/ML-airport-configuration

Donation is the normal term for proejcts joining the foundation - in doing so you establish a steering committee where members are entitled to voting rights. To graduate as an incubation project, 5 organisations need to join your board and the project is thus the priorities of the project will no longer be driven by just one organisation.

In the short term there is still a full time internal team staffed and maintaining the project, but excitingly we now have a mechanism for new collaborators to properly come onboard.

Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best-practice and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.

Join users such as NASA, Telkomsel, XP Investimentos, Sber and Beamery!

Check out the repository here: https://github.com/kedro-org/kedro/

It's for interactive sessions not background ones

Are there any frameworks for avoiding premature optimisation? I'm looking for some content to share with some new teams.

How do you compare yourselves to Monte Carlo Data?

I'd take a lazy, typed data manipulation language over pandas all day

This example doesn't demonstrate any of the power of Cypher, the whole point is matching a pattern than corresponds to a traversal, which as it happens is much much more elegant than recursive SQL

The national football team also did really well in the Euro competition - this forced a lot of drunk people indoors and onto public transport.

It's a platform play where pipelines would be built and maintained

Kedro, Quantumblack Labs | Python Software Engineer | London | REMOTE currently, ONSITE ? | Full-time Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best-practice and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.

Kedro is listed on the 2020 ThoughtWorks Technology Radar (https://mattturck.com/data2020/) and has been framed as the "React for Data Science".

Check out our repository: https://github.com/quantumblacklabs/kedro

Please apply here mentioning Kedro + HN in your application: https://rb.gy/1njxa1

We are at an exciting juncture in the maturing ML and MLOps space. We are keen to expand our function set through more sophisticated deployments and integrations with other ecosystem tools.

dbdiagram.io for ERDs these days:

https://dbdiagram.io/home

Kedro, Quantumblack Labs | Python Software Engineer | London | REMOTE currently, ONSITE ? | Full-time

Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering best-practice and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.

Kedro is listed on the 2020 ThoughtWorks Technology Radar (https://www.thoughtworks.com/radar/languages-and-frameworks/...) and the 2020 Data & AI Landscape (https://mattturck.com/data2020/) and has been framed as the "React for Data Science".

Check out our repository: https://github.com/quantumblacklabs/kedro

Please apply here mentioning Kedro + HN in your application: https://rb.gy/1njxa1

We are at an exciting juncture in the maturing ML and MLOps space. We are keen to expand our function set through more sophisticated deployments and integrations with other ecosystem tools.