MLOps Orchestration and Engineering
Master the engineering foundations of production Machine Learning. Build local simulation environments, implement automated quality gates, and orchestrate containerized microservices step-by-step.
Welcome to MLOps Orchestration and Engineering. This sandbox provides a comprehensive, interactive curriculum designed to bridge the gap between localized model experimentation and distributed, production-grade software engineering.
Machine learning systems require more than just accurate algorithms; they demand robust infrastructure to handle data lineage, model artifact versioning, safe deployment strategies, and continuous statistical monitoring. Through these interactive labs, you will construct a complete end-to-end MLOps architecture from the ground up, moving from raw data extraction to live, monitored inference APIs.
🛠️ Local Environment Quick Start
To execute any interactive tutorial script locally on Linux or WSL:
$ git clone https://github.com/sadatian/MLOps.git && cd MLOps
# 2. Synchronize all dependencies inside isolated virtual environment
$ uv sync
# 3. Execute any interactive percent-celled python tutorial
$ uv run python src/intro.py
🧭 Curriculum Roadmap
Modern Python Environment & Dependency Control with uv
Lightning-fast virtual environments and synchronized workspace lockfiles.
Notebook Documentation with Jupytext & MkDocs
Execute scripts natively in cells and auto-compile them into clean web docs.
Cloud Services Simulations and Mock Servers
Interact with S3 locally inside python cells using Moto context mocks.
Data Versioning with DVC
Track large datasets and store metadata outside of Git repositories.
Experiment Tracking & Model Registry with MLflow
Log metrics, trace hyper-parameters, and register trained models in S3.
Integrated MLOps Pipeline (DVC + MLflow)
Trigger structured pipeline runs linking DVC tracked assets and MLflow logs.
Model Serving API with FastAPI
Package model inference scripts behind lightweight REST API JSON requests.
Model Deployment & Containerization with Docker
Compile a secure Docker image for the prediction service and run checks.
Model Monitoring & Data Drift Detection with evidently
Generate HTML dashboards and identify feature/prediction distribution shifts.
Continuous Integration for Machine Learning (CI/ML)
Enforce programmatic tests to flag metrics regression before release.
GitHub Actions CI Pipeline for MLOps
Orchestrate verification, data preparation, pipeline runs, and docker builds.
Pipeline Orchestration & DAGs (Airflow / Prefect)
Simulate production orchestrators like Airflow and Prefect with DAG run retry configurations.
Feature Store Implementation (Simulated Feast)
Avoid train-serve skew and query online/offline databases for time-travel features.
gRPC Serving, Batch Inference & Release Strategies
Compare REST and gRPC protocols, and run canary/shadow deployments.
Continuous Training (CT) & HITL Fallbacks
Setup automated retraining schedules with human-in-the-loop fallback overrides.
LLMOps & Generative AI Pipelines
Trace prompts, evaluate RAG quality metrics, and control LLM token costs/latency.
Infrastructure as Code (IaC) & Advanced Containerization
Provision resources declaratively and build GPU-accelerated runners.
(Upcoming) Agile MLOps Lifecycle & Heuristic Baselines
Draft sprint metrics, deploy simple rule-based heuristics, and manage project scope.