Amazon API Gateway
AWS Lambda
Postman
Apigee
Django REST framework
RapidAPI for Mac
Amazon S3
Gravitee.io
Metaflow
Apache Airflow
Luigi
Azkaban
DepHell
Kedro
n8n.io
RunDeck
Amazon API Gateway MetaflowBased on our record, Amazon API Gateway should be more popular than Metaflow. It has been mentiond 108 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.
An API Gateway: Routes the Google OAuth redirect to the token exchange Lambda function. - Source: dev.to / 13 days ago
AWS API Gateway is Amazon’s managed gateway service, designed to work seamlessly within the AWS ecosystem. It supports both REST and WebSocket APIs, with HTTP APIs being the lightweight, lower-cost option for simple proxying and routing use cases. - Source: dev.to / about 1 month ago
This opens up a world of customization options for controlling app access. For example, we can embed custom data in the ID token for the front-end client to use, enabling guards to restrict content. Alternatively, we can add custom scopes to the access token and implement fine-grained access control in an API Gateway API. All it takes is some Lambda function code, and Cognito triggers it at the right time. - Source: dev.to / about 2 months ago
When the built-in Amazon API Gateway authorization methods don’t fully meet our needs, we can set up Lambda authorizers to manage the access control process. Even when using Cognito user pools and Cognito access tokens, there may still be a need for custom authorization logic. - Source: dev.to / 2 months ago
The API Gateway includes an endpoint structured like this:. - Source: dev.to / 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
AWS Lambda - Automatic, event-driven compute service
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Postman - The Collaboration Platform for API Development
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
Apigee - Intelligent and complete API platform
Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.