I personally think your approach could be great.

Netflix has been using Metaflow internally for about two years, so we have many war stories :)Hi omarhaneef, It's closed-source so much harder to learn about without learning from people that have paid for it.Why not use secrets manager for this? Dask? Looking forward to exploring it.How would you say this aspect Metaflow compares to Git LFS (Also, thank you to the Netflix team for creating such a wonderful library and for making it open source.Is the main feature the fact I can quickly put my workflows into the cloud?- Metaflow snapshots your code, data, and dependencies automatically in a content-addressed datastore, which is typically backed by S3, although local filesystem is supported too. We will fix these links. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. If there is only one thing to do right, then it´s to not bet on one tool but keep the whole stack flexible.I am still missing well established standards for data formats, workflow definitions and project descriptions - hopefully open source ninjas will deliver on this front before proprietary pirats will destroy the field with progress-inhibiting closed things. I love that this allows you to transparently switch "runtime" from local to cloud, like spark does, but integrated with common python tools like sklearn/tf etc. It's also useful for real-time displays, where you can bind market and UI inputs to nodes and calculated nodes back to the UI - some things you want to recalculate frequently, whereas some are slow and need to happen infrequently in the background.I am disappointed that when I click on documentation, "why metaflow," I get a bunch of cartoony BS instead of a simple text explanation. It can even rotate the secret with not much headache./edit: I could have a wrapper script that reads the secret and then os.execve()...Can you please explain how you were able to better the performance of aws cli.Our S3 client just handles multiple worker processes correctly with error handling.Can you say a little about which niche this would occupy, and what the motivation is? If you had your cloud settings configured, the data snapshots would exist within an AWS S3 Bucket, and run metadata would be loaded into a Metadata service powered by RDS (Relational Data Store). We are a bit on the fence about it internally.This is pretty interesting approach. I have three questions: At my company we're on Kubeflow/Argo now, but things are developing quite a lot in this space so keen to not be myopic.Currently using DVC, MLflow just for metadata visualization and notes on experiments, and Anaconda for (python) dependency management. In that case, is Metaflow useful?4. Do you support, or plan to support, features commonly found in data science platform tools like Domino (Good question. Other important parts are dependency management, cloud integration, state transfer, inspecting and organizing results - features that are central to data science workflows.

- minus the input spec being not YAML but more language native (pythonic for e.g.

Metaflow intends to solve a different problem of providing an excellent development and deployment experience for ML pipelines.> Our execution model also supports arbitrary docker containers (on AWS batch) where you can theoretically bake in your own dependencies.That's fair, but it doesn't seem to be something encouraged by the framework, and that's fine.Maybe anti-UI is too strong yeah. with this OSS launch.3. "Edit: just went to the Amazon CodeGuru homepage. Happy to answer any questions! A first glance looking at Prefect, Metaflow, MLflow, Kedro and Dagster vs Airflow and Luigi below the surface - It's a python library for creating & executing DAGs- Each node is a processing step & the results are stored after each step so you can restart failed workflows from where it failed- Tight integration with AWS ECS to run the whole DAG on cloudI don't know why their .org site oddly feels like a paid SaaS tool. Congratulations!Metaflow comes with a built in scheduler. aws CLI today easily.- We have spent time and effort in keeping the API surface area clean and highly usable. Metaflow has pretty nice code artifact + params snapshotting functionality which is a core selling point. This seems like precisely the kind of tool I‘ve been looking for. Don't really need to track experiments, just looking for an easy way to deploy my models to Fargate.Yes indeed. What's the closest open-source alternative to Metaflow on the market? GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.By clicking “Sign up for GitHub”, you agree to our Metaflow is an new workflow tool developed by a team at Netflix. What was the design consideration associated with how certain kinds of objects get checkpointed?To be clear, the fundamental motivation behind these lines of questioning is: how can I leverage Metaflow in conjunction with existing python-based _on-prem_ distributed (or parallel) computing utilities, e.g. ; concepts - collaboration, versioning, archiving, dependency management)the workflow DAG is a core concept of Metaflow but Metaflow is not just that. What we offer is a way to iterate and productionize your models written using any of the aforementioned libraries (and more). What utility does it offer that other tooling doesnt or at least how has netflix extracted value? Could you please explain why yes or why no :) Collaborating with your self is also another scenario when Metaflow can be useful since it takes care of versioning and archiving various artifacts.Re 4, aren't Kubeflow and Lyft's recently open-sourced "Flyte" pretty similar?That's true of Kubeflow.

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