Based on our record, Metaflow should be more popular than Jenkins. 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.
Jenkins is an open-source automation server used for software continuous integration and delivery. It automates various tasks, such as building, testing, and deploying applications. It is easily extendable due to its vast ecosystem of plugins, making it easy to integrate into version control systems like Git, build tools like Maven/Gradle, and deployment platforms like AWS and Docker. - Source: dev.to / 3 months ago
It will give you a possibility to find and solve problems faster, release more stable and higher quality products. Here we will use CircleCI, but you can use whatever you need (Jenkins, Travis CI, GitLab CI). - Source: dev.to / about 1 year ago
CloudBees Jenkins Platform is a commercial offering from CloudBees, it is not the Jenkins project itself (which is open source). Jenkins is alive and well. See https://jenkins.io. Source: almost 2 years ago
Ok. I'm talking about this: https://jenkins.io/. Source: over 2 years ago
Currently supported : Datadog, Jenkins, DNS, HTTP. Source: over 2 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 / 7 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 / 10 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: over 2 years ago
CircleCI - CircleCI gives web developers powerful Continuous Integration and Deployment with easy setup and maintenance.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Travis CI - Simple, flexible, trustworthy CI/CD tools. Join hundreds of thousands who define tests and deployments in minutes, then scale up simply with parallel or multi-environment builds using Travis CI’s precision syntax—all with the developer in mind.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.