Deploy, Evaluate and Create AI Systems

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Deploy, Evaluate and Create AI Systems

This course is part of multiple programs.

Instructor: Hurix Digital

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Pre-deployment dependency checks prevent runtime failures by validating container setups and dependency graphs for reliable AI deployment.
Deployment decisions require evaluating performance, latency, and cost together against application needs and business constraints
Zero-downtime strategies like blue-green deployments are essential for production AI to maintain availability and allow quick rollback.
Choosing the wrong deployment target or release strategy creates technical debt that grows costly to fix over time.

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There are 3 modules in this course

Course Description: Deploy, Evaluate, and Create AI Systems

Did you know that nearly 70% of AI models never make it to production due to deployment issues like version conflicts, poor scaling, and downtime during updates? Reliable deployment is the key to transforming prototypes into production-grade AI systems. This Short Course was created to help ML and AI professionals deploy AI systems reliably in production, optimize deployment costs and performance, and implement zero-downtime release strategies for mission-critical AI services. By completing this course, you will be able to analyze, evaluate, and create scalable AI deployment pipelines using containerization, cloud orchestration, and blue-green deployment methods—skills you can immediately apply to ensure seamless, high-performance model releases. By the end of this course, you will be able to: • Analyze dependency graphs and container configurations to detect version conflicts. • Evaluate performance, latency, and cost metrics across deployment targets. • Create a blue-green deployment strategy for zero-downtime model upgrades. This course is unique because it blends DevOps principles with AI engineering, giving you practical experience in managing version control, optimizing system performance, and achieving continuous AI delivery without service interruptions. To be successful in this project, you should have: • Docker containerization experience • Cloud deployment fundamentals • Basic Kubernetes knowledge • ML/AI model deployment concepts

Learners will master the critical skill of identifying and preventing dependency conflicts before deployment by analyzing Dockerfiles, SBOM reports, and dependency graphs to catch version mismatches that cause runtime failures.

What's included

3 videos1 reading1 assignment

3 videos Total 14 minutes

Why Dependency Analysis Saves Production Deployments 3 minutes
Understanding Container Dependencies and Version Conflicts 6 minutes
Analyzing Dockerfiles and SBOM Reports for Dependency Conflicts 5 minutes

1 reading Total 10 minutes

Systematic Approach to Container Dependency Validation 10 minutes

1 assignment Total 3 minutes

Dependency Analysis Knowledge Check 3 minutes

Learners will master data-driven deployment decision-making by benchmarking AI systems across different deployment targets, analyzing performance-cost trade-offs, and selecting optimal infrastructure based on specific application requirements and business constraints.

What's included

3 videos1 reading2 assignments

3 videos Total 21 minutes

Why Deployment Target Selection Determines AI System Success 2 minutes
Performance Metrics and Cost Analysis for Deployment Targets 6 minutes
Benchmarking AI Models Across Deployment Targets 13 minutes

1 reading Total 10 minutes

Systematic Benchmarking and Cost Analysis for AI Deployment Targets 10 minutes

2 assignments Total 18 minutes

Performance Benchmark Dashboard Creation 15 minutes
Performance Analysis and Deployment Target Selection 3 minutes

Learners will master the design and implementation of blue-green deployment strategies that enable zero-downtime model upgrades, including coordination protocols with SRE teams, traffic routing mechanisms, and rollback procedures for production AI systems.

What's included

3 videos1 reading3 assignments

3 videos Total 12 minutes

Why Zero-Downtime Deployments Are Non-Negotiable for Production AI 3 minutes
Blue-Green Deployment Architecture and Coordination Protocols 6 minutes
Deploying ML Models with Blue-Green Strategy in Kubernetes 3 minutes

1 reading Total 10 minutes

Implementing Blue-Green Deployments with Kubernetes 10 minutes

3 assignments Total 30 minutes

Comprehensive Deployment Strategy Evaluation 12 minutes
Blue-Green Deployment Strategy Design 15 minutes
Blue-Green Deployment Strategy Knowledge Check 3 minutes

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Hurix Digital

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