Amazon AWS
DigitalOcean
Microsoft Azure
Linode
Google Cloud Platform
GoDaddy
Hostinger
HostGator
Metaflow
Apache Airflow
Luigi
Azkaban
DepHell
Kedro
n8n.io
RunDeck
Amazon AWS MetaflowYou could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.
Based on our record, Amazon AWS seems to be a lot more popular than Metaflow. While we know about 446 links to Amazon AWS, we've tracked only 14 mentions of Metaflow. 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.
Teachers, freelancers, and inbox zero purists rejoice: I built EmailDrop, a one-click AWS deployment that turns incoming emails into automatic Google Drive uploads. With Postmark's new inbound webhooks, AWS Lambda, and a little OAuth wizardry, attachments fly straight from your inbox to your Google Drive. In this post, I’ll walk through how I built it using Postmark, CloudFormation, Google Drive, and serverless... - Source: dev.to / 18 days ago
AWS, short for Amazon Web Services, offers over 200 powerful cloud services. And among them, Amazon Q stands out as one of the best tools they’ve introduced recently. Why? Because it’s not just another AI, it’s your superpowered generative AI coding assistant that actually understands how developers work. - Source: dev.to / 20 days ago
Create an AWS Account: If you don’t already have one, sign up at aws.amazon.com. The free tier provides 750 hours per month of a t2.micro or t3.micro instance for 12 months. - Source: dev.to / 28 days ago
Sign in to your AWS account. If you’re new to AWS, you can sign up for the free tier to get started without any upfront cost. - Source: dev.to / about 2 months ago
Amazon Web Services (AWS) has completely changed the game for how we build and manage infrastructure. Gone are the days when spinning up a new service meant begging your sys team for hardware, waiting weeks, and spending hours in a cold data center plugging in cables. Now? A few clicks (or API calls), and yes — you've got an entire data center at your fingertips. - Source: dev.to / about 2 months 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
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.
