paradigm VS Mage

I built Paradigm from scratch to deploy any model at scale. It deploys the model on Kubernetes with load balancers. If you run into any issues, I'm happy to guide you on how to use it.

I started with paradigm. I got a deeper understanding about argo workflows through it as well. Helps to get a proper grab of industry standards from the beginning.

For scalability, it should be on Kubernetes. This is the best solution I have come across. You can deploy the model as a service with a LoadBalancer. You can refer to Kubernetes services or use a tool such as this one that handles building the service for you.

I came across this tool today and checked it out, I feel this can get the job done very quickly without so many complex features. It is also very small in size, so does not take up a lot of space in the cluster as well.

Link - https://github.com/ParadigmAI/paradigm It seems to be a pretty new project. But this has high usability.

Prefect is a good choice, But I wanted a much simpler tool. Hence, I built a barebone workflow controller here.

In the coming years, Mage will create a cooperative experience so that developers can build data pipelines with their team and level up together. After that journey, Mage will go on an epic quest to create the 1st open world community experience in the data universe.

Link to original blog: https://www.mage.ai/blog/data-sources-ep-2-aws-s3-to-postgres-data-sync-using-singer

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

You're invited to participate in the very first Mage Battlegrounds: Craft insights from real-time customer behavior analysis, a 24-hour virtual hackathon hosted by Shashank Mishra! This data engineering competition will take place on Saturday, April 15, 2023 beginning at 11am (PST). This will be a global event open to all participants who register.

Try this feature: https://github.com/mage-ai/mage-ai/issues/1166

Seeing if we can use it in https://github.com/mage-ai/mage-ai

That being said there’s a solid family of services just breaking ground that make the local pipeline deployment easier (check out https://www.mage.ai, which does have a clear path to cloud deployment of locally developed pipes, it just isn’t well documented yet, and also https://www.neuronsphere.io - which doesn’t have a public solution YET (they’re internally testing an alpha) but they built a cloud deployable solution for their paying customers and working to release one for freemium use)

7️⃣ mage-ai/mage-ai

Yes, check out how Mage does it: https://github.com/mage-ai/mage-ai/tree/master/mage_integrations/mage_integrations/destinations/delta_lake_s3