graviraja/MLOps-Basics
MLOps Basics
MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline.
The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardized formats to ensure cross-platform interoperability.
The repository covers a broad range of operational capabilities, including continuous integration and delivery automation, hierarchical configuration management, and system log aggregation. It also addresses observability through experiment tracking, training progress monitoring, and the use of dashboards to detect data drift during production inference.
The project is implemented using Jupyter Notebooks and provides configuration for linking virtual environments to notebook kernels.
Features
- Serverless Deployment - Provides blueprints for deploying containerized machine learning models as scalable services using serverless infrastructure.
- MLOps Guides - Provides comprehensive instructional materials and practical workflows for the operational side of the machine learning lifecycle.
- ONNX Model Exports - Converts trained models into the standardized ONNX format to enable cross-platform interoperability and runtime compatibility.
- Model Inference - Provides frameworks and utilities for loading model checkpoints and generating predictions from input data.
- ML Asset Versioning - Implements systems for tracking versions of datasets and models to ensure reproducibility and iteration lineage.
- Model Training Orchestration - Implements the execution of the model training process using predefined requirements, scripts, and project configurations.
- Artifact Versioning - Implements a framework for versioning large binary models and datasets using external caching and metadata pointers.
- Model Inference Functions - Deploys containerized machine learning models as scalable, event-driven inference functions via serverless infrastructure.
- Large File Storage (LFS) - Tracks oversized binary models and datasets by offloading them to separate storage to maintain repository performance.
- Machine Learning Pipelines - Provides automated workflows that sequence data preprocessing, model selection, and evaluation for machine learning tasks.
- Model Configuration Management - Provides tools for managing and saving persistent model parameters and hyperparameters during the development lifecycle.
- Experiment Tracking - Provides tools for logging hyperparameters and performance metrics to evaluate and reproduce ML experiments.
- Training Progress Monitoring - Provides systems for tracking metrics such as loss and accuracy via external dashboards to evaluate training progress.
- ONNX Model Exporters - Provides guidance and tools for converting trained machine learning models into the standardized ONNX format.
- Model Containerization Tools - Provides utilities for packaging machine learning models and their dependencies into standardized Docker images for portability.
- Model Interoperability Formats - Provides strategies for transforming models into standardized representations to enable portability across different frameworks.
- Model Performance Decay Detection - Implements model performance decay detection by monitoring feature distributions to identify declines in prediction quality.
- CI/CD Pipelines - Implements automated CI/CD pipelines for the building, testing, and deployment of machine learning code.
- Docker Image Building - Provides procedures for building Docker images that package ML models and runtime environments for consistent execution.
- Containerized Deployments - Packages machine learning environments and code into portable container images for consistent execution across platforms.
- Containerized Execution Environments - Provides guidelines for packaging models and dependencies into isolated container environments to ensure consistent execution.
- Serverless Image Optimization - Builds optimized container images for cloud functions to deploy models as scalable, on-demand services.
- Hierarchical Configuration Systems - Provides a hierarchical system for organizing hyperparameters and settings to ensure experiment reproducibility.
- GitHub Actions - Uses GitHub Actions to automate the build, test, and deployment pipelines for containerized machine learning models.
- Log Aggregation - Integrates ELK-based systems to centralize and analyze inference logs for performance monitoring and drift detection.
- Inference Performance Monitoring - Implements telemetry for tracking model-serving metrics and analyzing inference logs for production performance.
- ML Inference Dashboards - Provides a system for aggregating inference logs and visualizing model performance to detect production data drift.
- Machine Learning Operations - Foundational guide and resources for MLOps practices.
- MLOps Articles - Foundational concepts for getting started with MLOps.
