A container registry backed by Workers and R2.

Serverless private npm registry using https://serverless.com/

Serverless reverse proxy for exposing container registries (GCR, Docker Hub, Artifact Registry etc) on custom domains.

Private Composer registry for private PHP packages on AWS Serverless

v2.0 - ScaleCube Services provides a low latency Reactive Microservices library for serverless service registry and discovery based on gossip protocol and without single point-of-failure or bottlenecks.

3-Pipeline LLMOps Financial advisor. Steaming pipeline deployed on AWS, 24/7 collects, embeds live-data into QdrantDB. Training pipeline finetunes model on serverless GPU and logs best model on WandB Registry.Inference pipeline downloads best model from registry for inference, utilizes Langchain to maintain history and context retrieval.

Scalable agent registry for AI agents using A2A protocol AgentCard with semantic search. AWS serverless (Lambda, S3 Vectors, Bedrock). Python SDK & React Web UI.

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We train and deploy a serverless Sentiment Analysis API to GCP by using BERT (DistilBERT), TensorFlow, FastAPI, Python, Google AI Platform Training, Google Storage, Cloud Build, Cloud Container Registry, and Cloud Run.

An end to end ML project. Using MLflow for experiment tracking and model registry. Prefect for workflow orchestration. S3 for artifacts storage. AWS Lambda/ ECR for serverless model serving. AWS REST API gateway as endpoint to lambda function. GitHub Actions for CI/CD.

Amazon OpenSearch Serverless module available on the Terraform registry.

Scaleway have a Serverless service in public beta. This GitHub Action uses the Scaleway API to deploy your images in the Scaleway Container Registry in to a Serverless Container.

Serverless Registry CLI （Serverless Devs Registry Component）

A serverless terraform registry

This project covers the end to end implementation of deploying and productionizing a dockerized/containerized machine learning python flask application into Kubernetes Cluster using the AWS Elastic Kubernetes Service (EKS), AWS Serverless Fargate Instances, AWS CloudFormation Cloud Stack and AWS Elastic Container Registry (ECR) Service. The machine learning business case implemented in this project includes a bank note authentication binary classifier model using Random Forest Classifier; which predicts and classifies a bank note either as a Fake Bank Note (Label 0) or a Genuine Bank Note (Label 1). Implementation Steps: 1. Creation of an end to end machine learning solution covering all the ML life-cycle steps of Data Exploration, Feature Selection, Model Training, Model Validation and Model Testing on the unseen production data. 2. Saved the finalised model as a pickle file. 3. Creation of a Python Flask based API; in order to render the ML model solution and inferences to the end-users. 4. Verified and tested the working status of the Python Flask API in the localhost set-up. 5. Creation of a Docker File (containing the steps/instructions to create a docker image) for the Python Flask based Bank Note Authentication Machine Learning Application embedded with Random Forest ML Classifier Model. 6. Creation of IAM Service Roles with appropriate policies to access the AWS Elastic Container Registry (ECR) Service and AWS Elastic Kubernetes Service (EKS) and AWS CloudFormation Service. 7. Created a new EC2 Linux Server Instance in AWS and copied the web application project’s directories and its files into the AWS Linux Server using SFTP linux commands. 8. Installed the Docker software and the supporting python libraries in the AWS EC2 Linux Server Instance; as per the “requirements.txt” file. 9. Transformation of the Docker File into a Docker Image and Docker Container representing the application; using docker build and run commands. 10. Creation of a Docker Repository within the AWS ECR Service and pushed the application docker image into the repository using AWS Command Line Interface (CLI) commands. 11. Creation of the Cloud Stack with private and public subnets using the AWS CloudFormation Service with appropriate IAM roles and policies. 12. Creation of the Kubernetes Cluster using the AWS EKS Service with appropriate IAM roles and policies and linked the cloud stack created using the AWS CloudFormation Service. 13. Creation of the AWS Serverless Fargate Profile and Fargate instances/nodes. 14. Creation and configured the “Deployment.yaml” and “Service.yaml” files using the Kubernetes kubectl commands. 15. Applied the “Deployment.yaml” with pods replica configuration to the AWS EKS Cluster Fargate Nodes; using the Kubernetes kubectl commands. 16. Applied the “Service.yaml” using the Kubernetes kubectl commands; to render and service the machine learning application to the end-users for public access with the creation of the production end-point. 17. Verified and tested the inferences of the productionized ML Application using the AWS Fargate end-point created in the AWS Kubernetes EKS Cluster. Tools & Technologies: Python, Flask, AWS, AWS EC2, Linux Server, Linux Commands, Command Line Interface (CLI), Docker, Docker Commands, AWS ECR, AWS IAM, AWS CloudFormation, AWS EKS, Kubernetes, Kubernetes kubectl Commands.

Web Hook Registry, and Invoker (Serverless)

Run a serverless Terraform registry from bucket storage

A private Terraform Registry backed by AWS serverless technologies, that can auto update based on GitHub webhooks

A serverless terraform registry for AWS

A CloudFormation template that creates a serverless architecture to implement NIP-05 and NIP-35, allowing users to discover public keys and relays for internet identifiers on AWS.

Serverless Regsitry Website

A serverless alternative registry for Rust.

A solution for 25 days of serverless to implement a holiday registry using serverless Azure Functions and Durable Functions.

📦📒 Serverless skpm registry

Note: This registry covers Serverless v0.5 plugins

Lightweight serverless schema registry for event validation

Serverless AWS registry

Serverless plugin to register function names with AWS SSM Parameter Store

A container registry, implemented with Lambda, DynamoDB, and S3

No description available