Machine Learning Model Deployment and Monitoring in Production: Building Scalable, Reliable AI Systems

About the Course

This comprehensive training program is designed for data scientists, machine learning engineers, and DevOps professionals who need to move beyond model development and into production-grade deployment. Organizations increasingly struggle with the gap between lab-based model training and real-world production challenges such as data drift, model degradation, infrastructure scaling, and continuous monitoring.

This course bridges that critical gap by providing hands-on experience with industry-standard tools and frameworks for deploying ML systems that are reliable, scalable, and maintainable. You'll work through real-world scenarios, best practices, and practical architectures used by leading technology companies to ensure their AI systems perform consistently in production.

Course Objectives

• Understand containerization and orchestration technologies (Docker, Kubernetes) for ML workloads
• Implement CI/CD pipelines specifically designed for machine learning models
• Deploy models using multiple frameworks and cloud platforms (AWS, Azure, GCP)
• Set up comprehensive monitoring and alerting systems for model performance
• Detect and respond to data drift, model drift, and performance degradation
• Implement feature stores and versioning systems for reproducibility
• Design scalable inference architectures handling high-traffic scenarios
• Establish governance, compliance, and audit trails for production models
• Optimize inference latency and resource utilization in production
• Implement rollback strategies and A/B testing frameworks

Target Audience

This course is ideal for:

• Machine learning engineers transitioning from research to production roles
• Data scientists responsible for deploying their own models at scale
• DevOps and platform engineers supporting ML infrastructure
• Solutions architects designing end-to-end ML systems
• Technical leads overseeing model lifecycle management
• Organizations seeking to formalize ML operations and governance

What You Will Benefit as a Learner

• Practical Skills: Hands-on experience deploying models using industry-standard tools and platforms
• Production Readiness: Knowledge to identify and fix common production failures before they impact business outcomes
• Career Advancement: MLOps expertise increasingly in-demand across organizations building AI systems
• Cost Optimization: Strategies to reduce infrastructure costs while maintaining performance and reliability
• Cross-functional Collaboration: Understand how to work effectively with data teams, infrastructure teams, and business stakeholders
• Competitive Advantage: Deploy models faster and with greater reliability than competitors, reducing time-to-value for AI initiatives

Training Methodology

• Hands-on Labs: Build complete deployment pipelines using real cloud infrastructure and containerization tools
• Case Studies: Analyze production ML systems from leading companies, discussing lessons learned and best practices
• Live Demonstrations: Watch expert instructors deploy models and set up monitoring in real-time, with Q&A opportunities
• Capstone Project: Develop an end-to-end ML system from model versioning through production deployment with monitoring
• Interactive Discussions: Collaborate with peers on common deployment challenges and troubleshooting scenarios
• Reference Architectures: Receive documented templates and code samples for common deployment patterns