🚀 Zenith - The Zenith of Machine Learning Platforms

The ultimate open-source AI-first MLOps platform for 2026 — combining enterprise-grade ML lifecycle management with cutting-edge LLM, RAG, and agent capabilities. Built to surpass Vertex AI, SageMaker, Azure ML, Databricks, MLflow, W&B, and more.

🎯 Architecture Overview

┌─────────────────────────────────────────────────────────────────┐ │ React 19 Frontend UI │ │ Experiments │ Models │ Deployments │ Monitoring │ Agents │ └────────────────────────┬────────────────────────────────────────┘ │ REST API / WebSocket ┌────────────────────────▼────────────────────────────────────────┐ │ FastAPI Backend (Async) │ │ Auth │ Projects │ Datasets │ Features │ Training │ Deploy │ └─┬──────┬──────┬──────┬──────┬──────┬──────┬──────┬──────┬──────┘ │ │ │ │ │ │ │ │ │ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ┌────┐┌────┐┌────┐┌────┐┌────┐┌────┐┌────┐┌────┐┌────┐┌────┐ │PG ││Redis││S3/ ││Triton││vLLM││Celery││Jupyter││OTel││Vector││Feature│ │SQL ││Cache││Blob││Serve││GPU ││Worker││Lab ││Export││DB ││Store │ └────┘└────┘└────┘└────┘└────┘└────┘└────┘└────┘└────┘└────┘

✨ Feature Comparison

Feature	Zenith	Vertex AI	SageMaker	Azure ML	Databricks	MLflow	W&B
Open Source	✅	❌	❌	❌	Partial	✅	❌
LLM-Native	✅	✅	✅	✅	✅	Partial	✅
Agent Orchestration	✅	Partial	❌	Partial	✅	❌	❌
Prompt Playground	✅	✅	❌	✅	✅	❌	✅
RAG Pipeline Builder	✅	❌	❌	❌	✅	❌	❌
OpenTelemetry Native	✅	Partial	Partial	Partial	❌	✅	❌
Feature Store (Online)	✅	✅	✅	✅	✅	❌	❌
Real-time Drift Detection	✅	✅	✅	✅	✅	❌	Partial
LLM-as-Judge Eval	✅	❌	❌	❌	✅	❌	✅
LoRA/QLoRA Fine-tuning	✅	✅	✅	✅	✅	❌	❌
Collaborative UI	✅	✅	✅	✅	✅	❌	✅
Self-Hosted	✅	❌	❌	❌	Partial	✅	❌
Cost	Free	$$$	$$$	$$$	$$$	Free	$$

🎁 Core Capabilities

ML/LLM Lifecycle Management

• Data Versioning: Immutable dataset snapshots with lineage tracking
• Feature Store: Online (Redis) + Offline (Parquet/Delta) with point-in-time joins
• Experiment Tracking: Parameters, metrics, artifacts, prompts, traces with real-time visualization
• Model Registry: Staging/production promotion with approval workflows and A/B testing
• Distributed Training: PyTorch FSDP/DDP, Hugging Face Accelerate, multi-GPU support
• Hyperparameter Optimization: Optuna Bayesian optimization + prompt search
• Deployment: Batch/real-time/streaming with autoscaling and canary releases
• Monitoring: Drift detection (Evidently), performance metrics, cost tracking
• Explainability: SHAP values, attention visualization, feature importance
• Governance: Bias detection, PII scanning, audit logs, RBAC

LLM & Agent Features

• Prompt Playground: Interactive testing with multiple models, temperature control, few-shot examples
• RAG Pipeline Builder: Visual editor for embedding, retrieval, reranking, generation
• Agent Orchestration: LangGraph/CrewAI-style workflows with tool integration
• Chain Tracing: OpenTelemetry-based distributed traces for complex LLM chains
• LLM-as-Judge: Automated evaluation using GPT-4, Claude for quality scoring
• Fine-tuning: LoRA, QLoRA with monitoring and automatic checkpoint management
• Vector Search: Integrated embedding storage and semantic search
• Hallucination Detection: Confidence scoring and fact verification

Developer Experience

• Modern UI: React 19 + Tailwind + shadcn/ui with dark mode
• Real-time Collaboration: Live experiment updates, shared notebooks
• Jupyter Integration: Embedded JupyterLab with SDK pre-installed
• REST + Python SDK: Comprehensive APIs for all operations
• OpenTelemetry Export: Send traces to Datadog, Grafana, Jaeger
• Plugin System: Custom evaluators, metrics, retrievers, agents
• One-command Deploy: Docker Compose or Kubernetes Helm

🚀 Quick Start

Prerequisites

• Docker 24+ & Docker Compose 2.20+
• 16GB RAM minimum (32GB recommended)
• NVIDIA GPU (optional, for LLM inference)

Installation

git clone https://github.com/yourusername/zenith-ml.git cd zenith-ml

cp .env.example .env

docker-compose up -d

docker-compose logs -f backend

Access Points

• Frontend UI: http://localhost:3000
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs
• JupyterLab: http://localhost:8888 (token: zenith)
• Triton Inference: http://localhost:8001

First Steps

from zenith import ZenithClient

client = ZenithClient("http://localhost:8000")

project = client.create_project( name="my-first-project", description="Testing Zenith capabilities" )

experiment = client.start_experiment( project_id=project.id, name="baseline-model" )

client.log_params({"learning_rate": 0.001, "batch_size": 32}) client.log_metrics({"accuracy": 0.95, "loss": 0.12})

client.log_model(model, name="my-model", framework="pytorch")

📊 Feature Deep Dive

Experiment Tracking

• MLflow-compatible API with superior UI
• Real-time metric streaming with WebSocket
• Side-by-side run comparison with diff views
• Nested runs for hyperparameter sweeps
• Artifact versioning with S3/MinIO backend
• Git integration for code versioning

Feature Store

• Online serving with Redis (<10ms latency)
• Offline storage with Parquet/Delta Lake
• Point-in-time correct joins for time-series
• Feature transformation pipelines
• Schema evolution and validation
• Feature lineage and impact analysis

Model Deployment

• Triton Inference Server integration
• vLLM for high-throughput LLM serving
• FastAPI endpoints with automatic OpenAPI
• A/B testing and canary deployments
• Autoscaling based on latency/throughput
• Multi-model serving with routing

Monitoring & Observability

• Data drift detection (Evidently AI)
• Model performance degradation alerts
• LLM-specific metrics (hallucination rate, toxicity)
• OpenTelemetry traces for debugging
• Cost tracking per model/endpoint
• Real-time dashboards with Recharts

Agent & RAG Workflows

• Visual workflow builder for agent orchestration
• Pre-built RAG templates (Q&A, summarization, etc.)
• Multi-hop reasoning with chain-of-thought
• Tool calling with automatic schema generation
• Human-in-the-loop approvals
• Workflow versioning and rollback

🏗️ Project Structure

zenith-ml/ ├── backend/ # FastAPI application ├── frontend/ # React 19 UI ├── jupyter/ # JupyterLab configuration ├── inference/ # Triton models ├── kubernetes/ # Helm charts ├── scripts/ # Utility scripts ├── examples/ # End-to-end tutorials ├── tests/ # Test suite └── docs/ # Documentation

🛠️ Technology Stack

Backend: FastAPI, SQLAlchemy 2, asyncpg, Redis, Celery Frontend: React 19, Vite, TypeScript, Tailwind CSS, shadcn/ui, Zustand, TanStack Query ML: PyTorch, Transformers, Accelerate, PEFT, Optuna, Evidently Inference: Triton, vLLM, llama.cpp Observability: OpenTelemetry, Prometheus, Grafana Storage: PostgreSQL, Redis, S3/MinIO Orchestration: Kubernetes, Celery, RQ

📚 Examples

• Tabular ML: XGBoost with feature store and drift monitoring
• Computer Vision: ResNet fine-tuning with distributed training
• LLM Fine-tuning: LoRA on Llama 3 for domain adaptation
• RAG Agent: Question-answering with retrieval and reranking
• Multi-modal: CLIP for image-text matching with monitoring

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

📄 License

Apache License 2.0 - see LICENSE file

🌟 Star History

⭐ Star us on GitHub to support the project!

📧 Support

• Documentation: https://zenith-ml.readthedocs.io
• Discord: https://discord.gg/zenith-ml
• Issues: https://github.com/yourusername/zenith-ml/issues

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