You 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 Algorithmia. While we know about 380 links to Amazon AWS, we've tracked only 5 mentions of Algorithmia. 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.
AWS (Amazon Web Services) is a comprehensive cloud computing platform provided by Amazon, offering a wide range of services including computing power, storage, and databases. It enables businesses and developers to access and use scalable and cost-effective cloud resources on-demand. - Source: dev.to / 2 days ago
Amazon Web Services (AWS) is one of the most popular cloud computing platforms worldwide. It offers a comprehensive suite of services that enable developers and businesses to build, deploy, and scale applications with ease. - Source: dev.to / 2 days ago
Before installing Quickwit, you'll need to create an object storage bucket to hold your Quickwit indexes. You can use use your choice of Cloud provider such as Scaleway, AWS S3 or MinIO. Refer to our official Quickwit documentation for storage configuration details. - Source: dev.to / 5 days ago
Having an AWS Account: Sign up for an AWS account at AWS if you don't already have one. This will be necessary for deploying your application to Amazon EC2. - Source: dev.to / 4 days ago
Create an AWS Account: Go to AWS and sign up for an account. - Source: dev.to / 4 days ago
To push a model into production, there are additional concerns which the tools in the versioning, deployment and release space aim to solve. This includes obtaining adequate infrastructure to run the model reliably and facilitating easy model release or rollback. Solutions in the MLOps space includes Kubeflow, Pachyderm and Algorithmia. - Source: dev.to / over 2 years ago
And for enterprises that want to do the same with ML you can use algorithmia.com. Source: over 2 years ago
Algorithmia advertises themselves as an MLops platform for data scientists, and they provide an easy way to host models on a scalable REST API. Source: over 2 years ago
Seems similar to https://algorithmia.com. Source: over 2 years ago
Algorithmia.com — Host algorithms for free. Includes free monthly allowance for running algorithms. Now with CLI support. - Source: dev.to / almost 3 years ago
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.
Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.
neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.
Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.Sign up to Linode through SaaSHub and get a $100 in credit!
MCenter - Machine Learning Operationalization