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Microservices - API Gateway / BFF

Consideraciones generales

Analizando la infraestructura a implementar en una "arquitectura intermedia" con .Net Core, estamos buscando una forma sencilla y eficiente de impolementar un API Gateway o BFF ( Backend for Frontend)

Explicación básica del porqué de un API Gateway

Red Hat

Red Hat propone un stack de herramientas para desarrollar aplicaciones basadas en Kubernetes, con el fin de brindar la capacidad de escalar de OnPremise al Cloud.

Red Hat 3scale API Management

Tutorial: Deploying a 3scale API gateway on Red Hat OpenShift e-book

Red Hat CodeReady Workspaces

.Net Core

Ocelot : API Gateway para .Net Core
El API Gateway de Ocelot implementa servicios de autenticación, logging, cache, etc.,

Cómo implementar puertas de enlace API con Ocelot.

Lo nuevo de Microsoft

.NET Microservices Sample Reference Application
This reference application is cross-platform at the server and client side, thanks to .NET Core services capable of running on Linux or Windows containers depending on your Docker host, and to Xamarin for mobile apps running on Android, iOS or Windows/UWP plus any browser for the client web apps. The architecture proposes a microservice oriented architecture implementation with multiple autonomous microservices (each one owning its own data/db) and implementing different approaches within each microservice (simple CRUD vs. DDD/CQRS patterns) using Http as the communication protocol between the client apps and the microservices and supports asynchronous communication for data updates propagation across multiple services based on Integration Events and an Event Bus (a light message broker, to choose between RabbitMQ or Azure Service Bus, underneath) plus other features defined at the roadmap.

Building a Backend for Frontend (BFF) For Your Microservices

Kestrel / IIS

Las API desarrolladas en .Net Core se pueden publicar en Kestrel web server que a su vez puede o no utilizar un reverse proxy como IIS.

Conclusiones

How to choose the right API Gateway for your platform.
¿Ocelot o Envoy? Queda pendiente de analizar y definir...

Explainable AI

8 Explainable AI Frameworks

Entre los frameworks mencionados se destacan:

What-if Tool: TensorFlow team announced the What-If Tool, an interactive visual interface designed to help visualize datasets and better understand the output of TensorFlow models.

LIME: Local Interpretable Model-Agnostic Explanations LIME is an actual method developed by researchers at the University Of Washington to gain greater transparency on what’s happening inside an algorithm.

DeepLIFT: DeepLIFT is a method that compares the activation of each neuron to its ‘reference activation’ and assigns contribution scores according to the difference.

AIX360: The AI Explainability 360 toolkit is an open-source library developed by IBM in support of interpretability and explainability of datasets and machine learning models.

Activation Atlases: Google in collaboration with OpenAI, came up with Activation Atlases, which was a novel technique aimed at visualising how neural networks interact with each other and how they mature with information along with the depth of layers.

44 Repositorios en github sobre explainable ai

Incluye: DALEX, AIX360, LIME, xai, DiCE, cxplain.

An overview of model explainability in modern machine learning

Towards a better understanding of why machine learning models make the decisions they do, and why it matters