open source platform for simplifying machine learning deployment

A cloud-agnostic ML Platform that will enable Data Scientists to run multiple experiments, perform hyper parameter optimization, evaluate results and serve models (batch/realtime) while still maintaining a uniform development UX across cloud environments

CB-Spider offers a unified view and interface for multi-cloud management.

Chat engine designed for millions of users. System design based on microservice architecture. Implementation based on cloud agnostic technologies. Configurable, observable, containerized and runnable, deployable local environment. Kafka, YugabyteDB, Cassandra, MinIO, HTTP, gRPC, Redis, .NET 8, ASP.NET, OpenTelemetry, Docker, Kubernetes.

Deploy server instances to different Cloud providers without rewriting Terraform files

Multi-cloud utilities and service initialization framework.

document store using cloud storage providers

My M.Sc. dissertation: Modern Data Platform using DataOps, Kubernetes, and Cloud-Native ecosystem to build a resilient Big Data platform based on Data Lakehouse architecture which is the base for Machine Learning (MLOps) and Artificial Intelligence (AIOps).

Cloud agnostic Ansible playbooks used to deploy vault cluster backed by raft storage engine in HA mode.Mainly used for setting up disposable clusters backed by LXC containers inside a Vagrant box.

A project to help you run container workloads the cloud-agnostic style

A repository for a java implemented AWS listener, intended to be used for those not allowed to use S3 events

QuickFaaS is a multi-cloud interoperability desktop tool targeting cloud-agnostic functions development and FaaS deployments ☁️