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Metaflow Alternatives
Similar projects and alternatives to metaflow
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flyte
Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale. It has been battle-tested at Lyft, Spotify, Freenome, and others and is truly open-source.
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InfluxDB
Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
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zenml
ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
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pytorch-lightning
Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
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clearml
ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
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Sonar
Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
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fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production
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Airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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Hystrix
Hystrix is a latency and fault tolerance library designed to isolate points of access to remote systems, services and 3rd party libraries, stop cascading failure and enable resilience in complex distributed systems where failure is inevitable.
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luigi
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better metaflow alternative or higher similarity.
metaflow reviews and mentions
Posts with mentions or reviews of metaflow.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-25.
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Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
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Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
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[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
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Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
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Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
- Building real world recommendation systems at scale
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DevOps Fundamentals for Deep Learning Engineers
MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb, https://airflow.apache.org/), and Luigi (used at Spotify, https://github.com/spotify/luigi). Then you have the model serving itself, so there is Seldon (https://www.seldon.io/), Torchserve (https://pytorch.org/serve/), and TensorFlow Serving (https://www.tensorflow.org/tfx/guide/serving). You also have the actual export and transfer of DL models, and ONNX is the most popular here (https://onnx.ai/). Spark (https://spark.apache.org/) still holds up nicely after all these years, especially if you are doing batch predictions on massive amount of data. There is also the GitFlow way of doing things and Data Version Control (DVC, https://dvc.org/) is taken a pole position there.
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Netflix Metaflow
The project predates Facebook’s name-change to Meta. That’s gotta be irksome.
Project site: https://metaflow.org
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A note from our sponsor - #<SponsorshipServiceOld:0x00007f160cbe3290>
www.saashub.com | 1 Apr 2023
Stats
Basic metaflow repo stats
20
6,527
9.4
4 days ago
Netflix/metaflow is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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