Techniques for deploying machine learning models as real-world services
Series Overview
This series is a practical educational content with a 4-chapter structure that allows you to learn Model Deployment step-by-step from the fundamentals.
Model Deployment is the final stage of a machine learning project and one of the most important steps. Even if you develop an excellent model, it cannot generate business value unless it operates stably in a production environment. You will systematically master essential technologies for practical work, from building REST APIs, containerization with Docker, deployment to cloud platforms, to monitoring and operations.
Features:
- ✅ From fundamentals to practice: Systematic learning from REST API design to cloud deployment
- ✅ Implementation-focused: Over 30 executable code examples, practical deployment patterns
- ✅ Multi-platform support: Covers major services on AWS, GCP, and Azure
- ✅ Production operation perspective: Knowledge necessary for actual operation such as monitoring, log management, and A/B testing
- ✅ Modern approaches: Utilizing the latest technologies such as Docker, Kubernetes, and serverless
Total Study Time: 80-100 minutes (including code execution and exercises)
How to Study
Recommended Learning Order
For beginners (no deployment experience):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 (all chapters recommended)
- Required time: 80-100 minutes
For intermediate learners (experience with machine learning and Web APIs):
- Chapter 2 → Chapter 3 → Chapter 4
- Required time: 60-70 minutes
Strengthening specific topics:
- REST API construction: Chapter 1 (intensive study)
- Cloud deployment: Chapter 3 (intensive study)
- Production operations: Chapter 4 (intensive study)
- Required time: 20-25 minutes/chapter
Chapter Details
Chapter 1: Deployment Basics
Difficulty: Beginner to Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Deployment Overview - MLOps pipeline, deployment patterns
- REST API Design - Endpoint design, request/response formats
- Inference API with Flask - Building a simple model serving server
- High-speed API with FastAPI - Type safety and automatic documentation generation
- Inference Server Construction - Batch inference, asynchronous processing, error handling
Learning Objectives
- ✅ Understand the importance of MLOps and deployment
- ✅ Understand basic REST API design principles
- ✅ Build model inference APIs with Flask and FastAPI
- ✅ Implement request validation and error handling
- ✅ Perform basic optimization of inference performance
Chapter 2: Containerization and Docker
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Docker Fundamentals - Containerization concepts, images and containers
- Dockerfile Creation - Containerizing ML environments, dependency management
- Multi-stage Builds - Image size reduction, efficient builds
- Docker Compose - Multi-container coordination, development environment setup
- Best Practices - Security, layer caching, optimization
Learning Objectives
- ✅ Understand basic Docker concepts and the benefits of containerization
- ✅ Create Dockerfiles for ML inference servers
- ✅ Optimize image size with multi-stage builds
- ✅ Coordinate multiple services with Docker Compose
- ✅ Build secure and efficient container images
Chapter 3: Cloud Deployment
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- AWS SageMaker - Model registration, endpoint creation, inference execution
- AWS Lambda - Serverless inference, cost optimization
- GCP Vertex AI - Custom model deployment, auto-scaling
- Azure Machine Learning - Managed endpoints, real-time inference
- Platform Comparison - Selecting cloud services according to use cases
Learning Objectives
- ✅ Deploy models with AWS SageMaker
- ✅ Implement serverless inference with AWS Lambda
- ✅ Serve custom models with GCP Vertex AI
- ✅ Build managed endpoints with Azure ML
- ✅ Select appropriate cloud services according to requirements
Chapter 4: Monitoring and Operations
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Log Management - Structured logging, log levels, log aggregation
- Metrics Monitoring - Prometheus, Grafana, custom metrics
- Model Drift Detection - Data drift, concept drift
- A/B Testing - Canary releases, gradual rollout
- Model Update Strategies - Continuous learning, retraining triggers, version control
Learning Objectives
- ✅ Record inference requests with structured logging
- ✅ Monitor inference metrics with Prometheus and Grafana
- ✅ Detect and handle data drift
- ✅ Safely validate new models with A/B testing
- ✅ Design and implement model update strategies
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain the importance of MLOps and model deployment
- ✅ Understand REST API design principles and best practices
- ✅ Explain the benefits and use cases of containerization
- ✅ Compare the characteristics of major cloud platforms
- ✅ Understand the monitoring components necessary for production operations
Practical Skills (Doing)
- ✅ Build inference APIs with Flask and FastAPI
- ✅ Containerize with Dockerfile and Docker Compose
- ✅ Deploy models on AWS, GCP, and Azure
- ✅ Build monitoring systems with Prometheus and Grafana
- ✅ Implement A/B testing and canary releases
Application Ability (Applying)
- ✅ Design appropriate deployment strategies according to project requirements
- ✅ Build ML systems that operate stably in production environments
- ✅ Diagnose and optimize performance issues
- ✅ Implement secure and scalable architectures
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Fundamentals: Variables, functions, classes, modules
- ✅ Machine Learning Basics: Flow of model training and inference
- ✅ Basic Web Knowledge: HTTP, REST API, JSON
- ✅ Command Line Operations: Basic terminal/shell commands
Recommended (Nice to Have)
- 💡 scikit-learn/TensorFlow/PyTorch: Model saving and loading
- 💡 Linux Basics: File operations, environment variables, process management
- 💡 Git/GitHub: Version control basics
- 💡 Cloud Basics: Basic concepts of AWS/GCP/Azure
Recommended prior learning:
- 📚 Supervised Learning Introduction Series (ML-A01) - Machine learning fundamentals
- 📚 MLOps Introduction Series (ML-C01) - Basic MLOps concepts (recommended)
Technologies and Tools Used
Main Frameworks/Libraries
- Flask 3.0+ - Lightweight web framework
- FastAPI 0.104+ - High-speed web API framework
- scikit-learn 1.3+ - Machine learning models
- TensorFlow/PyTorch - Deep learning models
- Prometheus - Metrics collection and monitoring
- Grafana - Visualization and dashboards
Infrastructure
- Docker 24+ - Containerization platform
- Docker Compose 2.20+ - Multi-container applications
- Kubernetes - Container orchestration (optional)
Cloud Platforms
- AWS - SageMaker, Lambda, ECR, CloudWatch
- Google Cloud Platform - Vertex AI, Cloud Run, Container Registry
- Microsoft Azure - Azure ML, Container Instances, Monitor
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook - Prototyping and validation
- VS Code / PyCharm - Code editor/IDE
Let's Get Started!
Are you ready? Start with Chapter 1 and master model deployment techniques!
Chapter 1: Deployment Basics →
Next Steps
After completing this series, we recommend proceeding to the following topics:
Deep Dive Learning
- 📚 Deployment with Kubernetes: KServe, Seldon Core, advanced orchestration
- 📚 Edge Deployment: TensorFlow Lite, ONNX, mobile/embedded devices
- 📚 Multi-Model Serving: Model routing, dynamic loading
- 📚 Federated Learning: Distributed learning and privacy protection
Related Series
- 🎯 MLOps Introduction (ML-C01) - Building complete ML pipelines
- 🎯 - Advanced monitoring and alerting
- 🎯 - Adversarial attacks and secure design
Practical Projects
- 🚀 Image Classification API Deployment - REST API and Dockerization of CNN models
- 🚀 Recommendation System - Real-time inference and A/B testing
- 🚀 Time Series Forecasting Service - Batch inference and scheduling
- 🚀 Multi-Model Platform - Integrated management of multiple models
Navigation
Update History
- 2025-10-23: v1.0 Initial release
Your model deployment journey starts here!