Rancher
Kubernetes
Terraform
Puppet Enterprise
Packer
Docker
RunDeck
HHVM
Metaflow
Apache Airflow
Luigi
Azkaban
DepHell
Activeeon
Kubernetes
StackStorm
Rancher MetaflowBased on our record, Rancher should be more popular than Metaflow. It has been mentiond 24 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.
I don't know in which extend you plan to use Kubernetes in the future, but if it is aimed to become several huge production clusters, you should looks into Apps like Rancher: https://rancher.com. Source: over 2 years ago
But I think once you have a good understanding of K8S internal (components, how thing work underlying, etc.), you can use some tool to help you provision / maintain k8s cluster easier (look for https://rancher.com/ and alternatives). Source: over 2 years ago
A few years, I would have said no. Now, I'm cautiously optimistic about it. Personally, I think that you can use something like Rancher (https://rancher.com/) or Portainer (https://www.portainer.io/) for easier management and/or dashboard functionality, to make the learning curve a bit more approachable. For example, you can create a deployment through the UI by following a wizard that also offers you... - Source: Hacker News / almost 3 years ago
Alternatively, it is also possible to use a multi-cloud or hybrid-cloud approach, which combines several cloud providers or even public and private clouds. Special tools such as Rancher and OpenShift can be very useful to run this type of system. - Source: dev.to / almost 3 years ago
Rancher provides a Rancher authentication proxy that allows user authentication from a central location. With this proxy, you can set the credential for authenticating users that want to access your Kubernetes clusters. You can create, view, update, or delete users through Rancher’s UI and API. - Source: dev.to / almost 3 years ago
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 / 6 months 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? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / 9 months 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: about 2 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: about 2 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: about 2 years ago
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Terraform - Tool for building, changing, and versioning infrastructure safely and efficiently.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Puppet Enterprise - Get started with Puppet Enterprise, or upgrade or expand.
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.