We don't think there is an exact equivalent as well. Would this be useful for me if I only need the deployment management part? > Metaflow has pretty nice code artefact + params snapshotting functionality which is a core selling point. Metaflow is an new workflow tool developed by a team at Netflix. We do follow a plugin architecture - so I’m hoping Kube happens sometime. Feel free to open a GitHub issue or reach out to us on http://chat.metaflow.org. In this article, terms of “pipeline”, “workflow”, and “DAG” are used almost interchangeably. I personally think your approach could be great. I have three questions: We might build some lightweight discovery / debugging UI later, if there's demand. You can achieve the same by doing some "xargs | aws cli" trickery but then error handling becomes harder. 2) Would you recommend using both Metaflow nad MLFlow in projects? I am not affiliated with the Meltano people, but I like the idea of keeping the system modular, what seems to make it easier to replace components. Integration with AWS services (Especially AWS Batch). with Meltano? In terms of dependencies, we went with conda because of its traction in the data science community as well as excellent support for system packages. 1. Thanks for open sourcing this! Next: 1)Current pipeline for the training and production is two separate pipeline which we want to combined, possibly use MLFlow, Airflow or KubeFlow. If I search "Scheduler" in your docs the top result is a roadmap item, and searching "Cron" turns up nothing. I have several questions. What we offer is a way to iterate and productionize your models written using any of the aforementioned libraries (and more). For small dataframes we just pickle for simplicity. For model monitoring, we haven’t found a good enough UI that can handle the diversity of models and use cases we see internally, and believe that notebooks are an excellent visualisation medium that gives the power to the end user (data scientists) to craft dashboards as they see fit. TensorFlow is an open source software library for numerical computation using data flow graphs. For instance, this tutorial example here (https://github.com/Netflix/metaflow/blob/master/metaflow/tut...) does not look substantially different to what I could achieve just as easily in R, or other Python data wrangling frameworks. Happy to help either through our gitter chat or help@metaflow.org. Yes - definitely think so. Provides built-in file/database access (read/write) wrappers as. The results can be pushed to various other systems. Metaflow provides similar features. Each node is either an input node with a provided value, or a computed node with a function to calculate its value. you need more connections than what a single AWS CLI process open to saturate network on a big box. How would you say this aspect Metaflow compares to Git LFS (https://git-lfs.github.com) and Data Version Control (https://dvc.org)? The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Is it true that right now that to run a DAG "every day at 3am UTC" requires an external service? 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.
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