Metaflow
Apache Airflow
Kupler
Workato
Luigi
Zapier
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Make.com
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My programming assignment, in general, is good, I got 81 out of 100, and I was planning to get more, but it is still I still want to work with your team, and looking forward to receiving good work from a great team. I received some notes about the PPT that it was very basic, but overall, I would really thank you for the quick response and actions from your side in a perfect timeline :)
Based on our record, Metaflow seems to be more popular. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform. - Source: dev.to / almost 2 years ago
As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Letโs find out by walking you through a sample project that helps you do so! Weโll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / about 2 years ago
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: over 3 years ago
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. Source: over 3 years ago
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. Source: over 3 years ago
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Kupler - Connect your tools, automate processes, and create custom workflows with Kupler. Built for flexibility, scalability, and control.
Workato - Experts agree - we're the leader. Forrester Research names Workato a Leader in iPaaS for Dynamic Integration. Get the report. Gartner recognizes Workato as a โCool Vendor in Social Software and Collaborationโ.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Zapier - Connect the apps you use everyday to automate your work and be more productive. 1000+ apps and easy integrations - get started in minutes.
Kubeflow - Kubeflow makes deployment of ML Workflows on Kubernetes straightforward and automated
