GenAI Application Architecture: Scalable & Secure AI Design

Explore what GenAI is and how it generates original content across text, images, video, and audio, with real‑life applications in writing, design, coding, natural language processing, and medical imaging.

Explore generative models trained on large data sets that create new data. Understand variational autoencoders' hidden structure and generative adversarial networks' generator-discriminator loop.

Explore variational autoencoders and generative adversarial networks, where the generator and discriminator compete in adversarial learning to create realistic data and detect fakes.

Discover how GenAI applications boost efficiency, automate work, and personalize recommendations, while powering drug discovery and material science, yet face bias, security, and computational costs.

Pablo invites you to check in by sharing reviews to reflect the course value and posting questions on the discussion board to foster a collaborative GenAI application architecture learning community.

Gates act as validation checkpoints between layers, enforcing secure, compliant data flow from the application layer to the model layer by verifying format, quality, and regulatory requirements.

Examine the LGPL architecture by breaking down layers, gates, pipes, and loops and how the application, model, data, and infrastructure layers form a scalable, secure AI design.

Learn how pipes, or pipelines, move data from the data layer to the model layer for pre-processing and training, and how inference pipelines handle user prompts for real-time results.

Leverage loops as feedback mechanisms to refine a scalable, secure GenAI architecture, with training, user feedback, and monitoring loops across layers.

Simulate a user feedback loop that adjusts a simple ai model based on gathered feedback, while validating credentials, handling document uploads, and performing summarization in the model layer.

Extend the prior simulation by executing all architecture layers—application, infrastructure, and model—to simulate login, encryption, cloud storage, summarization with OpenAI, and a feedback loop for scalable, secure genai design.

Learn how to design elastic infrastructure for GenAI applications with autoscaling, cloud platforms like AWS, GCP, and Azure, and containerization using Docker to ensure scalable, reliable performance.

Explore microservices versus monolith architectures for scalable AI application. Leverage containerization with Docker, APIs, and independent services with databases and deployment pipelines to improve modularity and fault tolerance.

Leverage load balancing to distribute traffic across multiple servers, boosting performance, scalability, and availability, while fault tolerance provides redundancy so a backup server can take over without downtime.

Discover how cloud native deployments on AWS, GCP, or Azure enable elastic, cost-effective, and efficient resource management to scale GenAI applications.

Leverage cloud elasticity to scale resources on demand, pay only for use, and quickly provision compute, storage, and managed services for agile, cost-effective GenAI apps.

Build resilient GenAI applications by planning for errors, logging details, enabling retries and rollbacks, and isolating failures to prevent cascading issues; complement with testing to simulate error scenarios.

Log errors without interrupting execution to enable debugging; isolate failed components to stop cascading failures; implement retry logic for corrective action to achieve success and guardrails for resilient architectures.

Attach performance, resource, and error monitoring to a JNI application, then use CloudWatch for logs and alerts, with X-Ray, CloudTrail, load balancing, and auto scaling for scalable insights.

Explore disaster recovery and high availability strategies for GenAI applications by identifying threats, implementing secure offsite data backups, and testing and validating recovery procedures.

Achieve high availability through redundancy and automatic failover, balance traffic with ELB, and enable disaster recovery using Route 53, auto scaling, S3 backups, and RDS multi-AZ.

Design a robust genai system for diagnosis recommendations by deploying a distributed, load-balanced, self-healing diagnosis engine across multiple regional hospitals. Ensure redundancy and self-healing mechanisms prevent overload and improve reliability.

Examine GenAI security threats, including model hijacking and privacy leakage, and learn to mitigate them with secure deployment protocols, encryption, audits, access controls, and privacy-preserving techniques.

Examine how generative adversarial networks enable deepfakes and evasion attacks, and learn defenses such as detection with watermarks, hardened input validation, adversarial training, role based access control, and explainable ai.

Explainable AI reveals how models reach decisions to fortify against adversarial attacks. It identifies biases and detects adversarial inputs to improve robustness and guide safety measures in AI applications.

Learn cost optimization strategies for gen AI infrastructure by right-sizing resources, leveraging spot instances, and containerization to reduce compute, storage, and memory costs.

Discover cost-saving strategies for generative AI apps, including model selection, quantization, pruning, and cloud pricing options with free tiers and trials to balance performance and cost.

Compare cloud platform options and gen AI features to optimize pricing for scalable AI applications, leveraging pay-as-you-go, reserved instances, and discounts for committed usage.