ConfigCat is a developer-centric feature flag service that helps you turn features on and off, change their configuration, and roll them out gradually to your users. It supports targeting users by attributes, percentage-based rollouts, and segmentation. Available for all major programming languages and frameworks. Can be licensed as a SaaS or self-hosted. GDPR and ISO 27001 compliant.
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Based on our record, ConfigCat should be more popular than Metaflow. It has been mentiond 54 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.
ConfigCat - ConfigCat is a developer-centric feature flag service with unlimited team size, excellent support, and a reasonable price tag. Free plan up to 10 flags, two environments, 1 product, and 5 Million requests per month. - Source: dev.to / 4 months ago
ConfigCat allows you to manage your feature flags from an easy-to-use dashboard, including the ability to set targeting rules for releasing features to a specific segment of users. These rules can be based on country, email, and custom identifiers such as age, eye color, etc. - Source: dev.to / 6 months ago
I recently started helping my friend @jordan-t-romero with a NextJS and NodeJS project she is working on. This weekend we incorporated ConfigCat so that we can add feature flags to control what content is displayed in the different environments (local, staging, production, etc.). - Source: dev.to / 12 months ago
But how can you be sure you’re making the right changes? It’s impossible to read your clients’ minds, but A/B testing might just be the next best thing. In this article, I’ll guide you through conducting an A/B test on an Android (Kotlin) application using ConfigCat’s feature flag management system and Amplitude. - Source: dev.to / 12 months ago
If you're planning on cutting back or saving bandwidth utilization and optimizing for better performance on the client side then a caching solution like Redis can help. And, as we've seen from the code examples, Redis integrates quite easily with ConfigCat. With a caching solution in place, you can supercharge the way you do standard feature releases, canary deployments, and A/B testing. Besides Node.js, ConfigCat... - Source: dev.to / 12 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 1 year 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 1 year 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 1 year ago
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... Source: over 1 year ago
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. - Source: dev.to / almost 2 years ago
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