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Interactive Labs & Code

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.

Start First Lab Explore Roadmap

Welcome to MLOps Orchestration and Engineering! This sandbox is designed to bridge the gap between model development and production-grade software engineering. By establishing automated pipelines, robust continuous integration, containerized microservices, and continuous observability, you ensure that machine learning systems are scalable, reproducible, and production-ready. Instead of writing standard markdown documentation or clicking through heavy Jupyter Notebooks, all tutorials in this project are written as fully executable, highly-celled Python (.py) scripts structured in the Jupytext percent format (# %%). This allows you to run them cell-by-cell in your favorite IDE (VS Code, PyCharm, etc.), while compiling into beautiful notebooks on this website.

🎯 Core Pillars & Tracks

⚙️

1. Infrastructure & Environments

Establish fully reproducible, local-first environments and simulated cloud services on your workstation.

  • Package Management: Synchronize deterministic virtual environments using ultra-fast uv lockfiles.
  • Jupytext Pipeline: Author fully executable, cell-structured Python (.py) files that automatically compile into clean documentation.
  • Simulated Cloud Storage: Mock model registries and dataset buckets locally utilizing boto3 and moto server contexts.
  • Infrastructure as Code: Provision local assets and resources declaratively using Terraform with compliance policies.
  • Hardware Portability: Abstract compute layers with automated CUDA GPU availability checks and CPU fallback routing.
🧪

2. ML Lifecycle & Pipelines

Orchestrate multi-stage pipelines, track experiments, and version dataset lineages without git bloat.

  • Data Version Control: Track large dataset pointers and metadata off-git using DVC to version model assets.
  • Experiment Tracking: Programmatically log hyperparameters, training metrics, and registered models using MLflow.
  • Reproducible Pipelines: Build multi-step cached pipeline DAGs using dvc.yaml to avoid redundant compute.
  • Workflow Orchestration: Construct Directed Acyclic Graphs (DAGs) using topological sorting for run execution.
  • DAG Fault Tolerance: Implement custom run retry delays, upstream task skipping, and validation cycles.
🚀

3. Serving & Containers

Deploy trained weights behind fast API interfaces, build optimized containers, and manage releases safely.

  • REST Inference APIs: Serve low-latency predictions via FastAPI with strict input/output Pydantic schemas.
  • gRPC Microservices: Implement high-throughput binary serialization using Protocol Buffers and gRPC.
  • Vectorized Micro-Batching: Queue concurrent REST/gRPC requests into optimized batched inference arrays.
  • Dockerization: Compile minimal, secure Alpine/Debian container runners using multi-stage Docker builds.
  • Advanced Release Strategies: Safely split traffic using canary releases, shadow deployments, and A/B test gates.
📊

4. CI/CD & Observability

Validate regression guardrails automatically in pull requests and monitor performance drift in production.

  • CI/ML Quality Gates: Enforce programmatic unit tests checking model RMSE and R2 bounds before container compilation.
  • CI/CD Pipelines: Automate unit testing, code linting, and image builds using containerized GitHub Actions workflow loops.
  • Statistical Monitoring: Detect covariate shift and feature/prediction distribution drift utilizing Evidently AI.
  • Continuous Training (CT): Set up automated MLflow retraining triggers linked to live concept drift observations.
  • LLMOps Observability: Log prompts, trace hierarchical spans, and monitor token usage and response latencies.

🧭 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.
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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.
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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.

🛠️ Local Environment Quick Start

To run any tutorial module code locally on your Linux or WSL distribution:

bash
# 1. Clone the repository and navigate into the workspace
$ git clone https://github.com/sadatian/MLOps.git && cd MLOps

# 2. Synchronize python dependencies inside virtual environment
$ uv sync

# 3. Execute any interactive percent-celled python tutorial
$ uv run python src/setup_warmup/module_01_environment_uv/uv_guide.py