MLOps Orchestration and Engineering
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MLOps Orchestration and Engineering
Home
๐ Live MLOps Tracking Dashboard
๐ ๏ธ Unified MLOps CLI Tutorial & Orchestration Guide
Setup
Setup
โ๏ธ Modern Python Environment & Dependency Control with
๐ Notebook Documentation with Jupytext & MkDocs
โ๏ธ Cloud Services Simulations and Mock Servers
Data & Experiment Tracking
Data & Experiment Tracking
๐ฆ Data Versioning with DVC
๐งช Experiment Tracking & Model Registry with MLflow
๐ Integrated MLOps Pipeline (DVC + MLflow)
Model Serving & Containers
Model Serving & Containers
๐ Model Serving API with FastAPI
๐ณ Model Deployment & Containerization with Docker
Production CI/CD & Monitoring
Production CI/CD & Monitoring
๐ Model Monitoring & Data Drift Detection with
๐ก๏ธ Continuous Integration for Machine Learning (CI/ML)
๐ค GitHub Actions CI Pipeline for MLOps
๐งญ Pipeline Orchestration & DAGs (Airflow / Prefect)
Advanced MLOps
Advanced MLOps
๐๏ธ Feature Store Implementation (Simulated Feast)
โก gRPC Serving, Batch Inference & Release Strategies
๐ Continuous Training (CT) & HITL Fallbacks
๐ง LLMOps & Generative AI Pipelines
๐ ๏ธ Infrastructure as Code (IaC) & Advanced Containerization
๐ Agile MLOps Lifecycle & Heuristic Baselines
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MLOps CLI Package
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