Kubeflow and MLFlow by EDGE AI

Tired of the MLOps "Stitching Problem"? Data science teams are forced to act like infrastructure engineers, spending more time managing Kubernetes, integrating tools, and fighting deployment errors than building models. Kubeflow & MLFlow by EDGE AI solves this. We deliver a complete, out-of-the-box MLOps platform, built on the zero-ops MicroK8s and Juju, that just works. What This Solves for You: Accelerate Time-to-Production: Go from a new model to a production endpoint in a fraction of the time. Unify Your ML Lifecycle: Use MLFlow to track every experiment, parameter, and metric. Then, use Kubeflow Pipelines to automate your entire workflow from data ingestion to model serving. Reduce Infrastructure Headaches: Powered by MicroK8s, our platform simplifies Kubernetes operations. Juju operators handle the complex integration and scaling of Kubeflow and MLFlow, so you don't have to. Enable True MLOps: Bridge the gap between your data scientists and your DevOps team with a single, governed platform for model development, deployment, and management. Key Features: One-Click Deployment: Get the full Kubeflow and MLFlow stack running in minutes. Automated Pipelines: Visually build and manage end-to-end ML pipelines with Kubeflow. Unified Experiment Tracking: Log, compare, and manage all your models and metadata with MLFlow. Effortless Kubernetes: A lightweight, conformant Kubernetes from MicroK8s for development and production. Enterprise-Grade Support: Backed by EDGE AI, you get a single point of contact for the entire stack.