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GenAI Application Architecture: Scalable & Secure AI Design
Published 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 40m | Size: 2.51 GB
Build scalable, secure, and efficient GenAI applications with AWS, MLOps, monitoring, and cloud-native architecture
What you'll learn
Design Scalable GenAI Applications: Learn to architect and build scalable GenAI applications using the LGPL architecture, focusing on Layer, Gate, Pipes
Implement Resiliency and Error Handling: Understand how to incorporate error handling, monitoring, logging, and disaster recovery to create resilient GenAI Apps
Ensure Security and Cost Efficiency: Develop secure and cost-effective GenAI solutions by leveraging AWS security services, containerization
Automate and Optimize with MLOps & CI/CD: Learn to implement MLOps, CI/CD, and Explainable AI (XAI) for streamlined deployment and future-proofing GenAI apps
Requirements
Basic Knowledge of AI and Machine Learning: Understanding of fundamental AI and machine learning concepts.
Familiarity with AWS: Experience with AWS services such as Lambda, S3, and DynamoDB is recommended.
Programming Skills: Intermediate-level knowledge of Python is essential.
Basic Understanding of Software Architecture: Familiarity with software architecture principles such as scalability, load balancing, and error handling.
Description
Master the essential techniques and best practices for designing and architecting scalable, secure, and cost-effective Generative AI (GenAI) applications. In this course, you'll explore the principles of the LGPL architecture (Layers, Gates, Pipes, and Loops) and how they apply to building GenAI systems using modern cloud services like AWS. We'll cover critical topics such as load balancing, containerization, error handling, monitoring, logging, and disaster recovery. This course is ideal for those looking to understand GenAI architecture, ensuring applications are resilient, secure, and efficient.What You'll Learn:Architect scalable and secure GenAI applications using the LGPL model.Understand core concepts such as containerization, load balancing, and disaster recovery.Learn best practices for monitoring, logging, and error handling in GenAI systems.Explore MLOps, CI/CD, and security strategies for future-proofing AI applications.This course focuses on the architecture and principles behind building robust GenAI systems, providing the knowledge needed to design effective AI solutions.Enroll now to transform your GenAI Application Architecture skills to the next level. Master GenAI Application Architecture - the core best practices and techniques for building secure, efficient, scalable GenAI Applications.Ready to take your skills to the next level? Join me, and let's get started. See you inside the course!
Who this course is for
AI Developers and Engineers: Those looking to build scalable, secure, and cost-effective GenAI applications.
Cloud Architects: Professionals working with AWS who want to implement GenAI architectures using best practices.
Machine Learning Enthusiasts: Individuals with a foundational understanding of machine learning and programming who want to expand into GenAI development.
Software Engineers: Engineers seeking to integrate AI into cloud-native applications and implement MLOps pipelines
Homepage:
Download link
rapidgator.net:
nitroflare.com:
Published 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 40m | Size: 2.51 GB
What you'll learn
Design Scalable GenAI Applications: Learn to architect and build scalable GenAI applications using the LGPL architecture, focusing on Layer, Gate, Pipes
Implement Resiliency and Error Handling: Understand how to incorporate error handling, monitoring, logging, and disaster recovery to create resilient GenAI Apps
Ensure Security and Cost Efficiency: Develop secure and cost-effective GenAI solutions by leveraging AWS security services, containerization
Automate and Optimize with MLOps & CI/CD: Learn to implement MLOps, CI/CD, and Explainable AI (XAI) for streamlined deployment and future-proofing GenAI apps
Requirements
Basic Knowledge of AI and Machine Learning: Understanding of fundamental AI and machine learning concepts.
Familiarity with AWS: Experience with AWS services such as Lambda, S3, and DynamoDB is recommended.
Programming Skills: Intermediate-level knowledge of Python is essential.
Basic Understanding of Software Architecture: Familiarity with software architecture principles such as scalability, load balancing, and error handling.
Description
Master the essential techniques and best practices for designing and architecting scalable, secure, and cost-effective Generative AI (GenAI) applications. In this course, you'll explore the principles of the LGPL architecture (Layers, Gates, Pipes, and Loops) and how they apply to building GenAI systems using modern cloud services like AWS. We'll cover critical topics such as load balancing, containerization, error handling, monitoring, logging, and disaster recovery. This course is ideal for those looking to understand GenAI architecture, ensuring applications are resilient, secure, and efficient.What You'll Learn:Architect scalable and secure GenAI applications using the LGPL model.Understand core concepts such as containerization, load balancing, and disaster recovery.Learn best practices for monitoring, logging, and error handling in GenAI systems.Explore MLOps, CI/CD, and security strategies for future-proofing AI applications.This course focuses on the architecture and principles behind building robust GenAI systems, providing the knowledge needed to design effective AI solutions.Enroll now to transform your GenAI Application Architecture skills to the next level. Master GenAI Application Architecture - the core best practices and techniques for building secure, efficient, scalable GenAI Applications.Ready to take your skills to the next level? Join me, and let's get started. See you inside the course!
Who this course is for
AI Developers and Engineers: Those looking to build scalable, secure, and cost-effective GenAI applications.
Cloud Architects: Professionals working with AWS who want to implement GenAI architectures using best practices.
Machine Learning Enthusiasts: Individuals with a foundational understanding of machine learning and programming who want to expand into GenAI development.
Software Engineers: Engineers seeking to integrate AI into cloud-native applications and implement MLOps pipelines
Homepage:
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rapidgator.net:
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nitroflare.com:
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