Based on our record, Google Kubernetes Engine should be more popular than Metaflow. It has been mentiond 45 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 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
Google Kubernetes Engine (GKE) is another managed Kubernetes service that lets you spin up new cloud clusters on demand. It's specifically designed to help you run Kubernetes workloads without specialist Kubernetes expertise, and it includes a range of optional features that provide more automation for admin tasks. These include powerful capabilities around governance, compliance, security, and configuration... - Source: dev.to / 6 days ago
Cloud Clusters: If you'd rather work in a cloud environment, consider platforms like Google Kubernetes Engine (GKE) or Amazon EKS for managed Kubernetes clusters. - Source: dev.to / 9 days ago
In this article, we’ll look at one of the ways to monitor the InterSystems IRIS data platform (IRIS) deployed in the Google Kubernetes Engine (GKE). The GKE integrates easily with Cloud Monitoring, simplifying our task. As a bonus, the article shows how to display metrics from Cloud Monitoring in Grafana. - Source: dev.to / 26 days ago
Set up a remote Kubernetes cluster. For this tutorial, Google Kubernetes Engine (GKE) was chosen; however, feel free to use any remote Kubernetes cluster. - Source: dev.to / about 2 months ago
Docker swarm still exists, it still works, and some of these other container orchestrators are still hanging on, but for the most part, you’re using Kubernetes if you’re doing this stuff at work. Generally it's well-understood that kubernetes is hard to get right, and so most people use it via a managed provider like Elastic Kubernetes Service from AWS, Azure Kubernetes Service from MSFT, or Google Kubernetes... - Source: dev.to / 4 months ago
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.
OpenShift Container Platform - Red Hat OpenShift Container Platform is the secure and comprehensive enterprise-grade container platform based on industry standards, Docker and Kubernetes.