Machine Learning Model Serving & API Deployment Training Course

Course Overview

Learning Objectives

• By the end of this training course, participants will be able to:
• Understand the principles and architectures of machine learning model serving.
• Evaluate different model inference and serving approaches according to business and technical requirements.
• Design APIs for exposing machine learning models as production services.
• Prepare and package trained models for deployment.
• Implement real-time and batch inference architectures.
• Apply appropriate deployment strategies for machine learning APIs.
• Configure authentication, authorization, and security controls for model-serving services.
• Manage API versions, model versions, and service releases.
• Monitor model-serving performance, latency, availability, and resource utilization.
• Diagnose API, infrastructure, and model-serving failures.
• Optimize machine learning services for scalability and operational efficiency.
• Develop a practical framework for deploying and managing production-ready machine learning APIs.

Who Should Attend

Learning Outcomes

• Upon successful completion of the course, participants will be able to:
• Explain the architecture and operational requirements of machine learning model serving.
• Select appropriate model-serving patterns based on application requirements.
• Design APIs that expose machine learning inference capabilities effectively.
• Package trained models and prepare their runtime environments.
• Implement real-time, batch, and asynchronous inference services.
• Deploy machine learning APIs across development, testing, and production environments.
• Apply authentication, authorization, encryption, and access-control mechanisms.
• Implement model and API versioning strategies.
• Establish monitoring and logging for model-serving services.
• Analyze latency, throughput, availability, and resource utilization.
• Troubleshoot deployment, API, infrastructure, and inference-related issues.
• Optimize machine learning APIs for scalability, reliability, and production performance.

Course Outline