Course Format & Delivery Details Learn On Your Terms – Self-Paced, Immediate Online Access, and Lifetime Learning
Our premier course, *Mastering AWS Solutions Architecture for AI-Driven Enterprises*, is meticulously structured to fit seamlessly into your professional journey, no matter your location, time zone, or schedule. This is a self-paced learning experience, designed for real-world professionals who demand flexibility without sacrificing depth or quality. The moment you enroll, you gain immediate online access to the full suite of course resources, allowing you to begin your transformation immediately-on your terms. No Deadlines, No Pressure – Learn On-Demand Anytime
There are no fixed start dates, no required login times, and no arbitrary deadlines. The entire course is delivered on-demand, giving you complete control over your learning rhythm. Whether you're balancing a demanding job, international time zones, or family responsibilities, this course adapts to you-not the other way around. You decide when to learn, how fast to progress, and where to focus your attention. Accelerated Path to Results – See Tangible Progress in Days
Most learners complete the full curriculum in 6 to 8 weeks with consistent effort. However, many report applying core architectural principles and designing scalable AI-ready systems within the first 10 days. Every module is engineered for speed-to-competence, delivering actionable insights you can implement the same day. This isn’t theoretical fluff-it’s a practical, ROI-driven roadmap to career advancement and technical mastery. Lifetime Access – Never Pay for Updates Again
Enrollment grants you lifetime access to the course content, including all future updates at no additional cost. As AWS evolves and AI architectures advance, your knowledge stays current. You’ll receive ongoing enhancements, new case studies, updated best practices, and expanded integration patterns-automatically, permanently, and free of charge. This is a one-time investment in a skillset that compounds over time. Available Anywhere, Anytime – 24/7 Global, Mobile-Friendly Access
Access your learning materials anytime, from any device. Our platform is fully mobile-responsive, ensuring you can study on your phone during a commute, review architectures on your tablet in a meeting, or dive deep into deployment workflows from your laptop at home. With 24/7 global availability, your career advancement is never limited by location or device. Direct Instructor Guidance – Expert Support When You Need It
You’re not learning in isolation. Throughout the course, you’ll have access to direct instructor support, including expert feedback, clarification on complex architectural decisions, and guidance on implementation challenges. Our certified AWS architects and AI infrastructure specialists are here to help you solve real problems, refine your designs, and ensure your learning translates into real-world success. Prove Your Mastery – Earn a Globally Recognised Certificate of Completion
Upon finishing the course, you will earn a Certificate of Completion issued by *The Art of Service*, a globally respected authority in professional certification and enterprise training. This credential is recognised by employers, consulting firms, and cloud teams worldwide. It validates your ability to design, deploy, and manage AWS solutions specifically tailored for AI-driven enterprises-giving you a competitive edge in hiring, promotions, and client engagements. Transparent, Upfront Pricing – No Hidden Fees, No Surprises
Our pricing model is refreshingly straightforward. What you see is exactly what you pay-no hidden fees, no recurring charges, no upsells. You pay one inclusive fee and gain full access to every resource, tool, and update. No tricks, no traps, just clarity and value. Secure, Trusted Payment Options – Visa, Mastercard, PayPal
We accept all major payment methods, including Visa, Mastercard, and PayPal. Our payment system is fully encrypted and compliant with the highest global security standards, ensuring your transaction is fast, secure, and hassle-free. Zero-Risk Enrollment – Satisfied or Refunded Guarantee
We stand behind the transformative power of this course with a strong satisfaction guarantee. If you complete the first two modules and feel this isn’t delivering the clarity, career leverage, or technical depth you expected, simply reach out. We’ll refund your investment-no questions, no delays. This is our commitment to your success and peace of mind. What to Expect After Enrolment – Confirmation and Access
After enrollment, you’ll receive a confirmation email acknowledging your registration. Your access credentials and detailed course entry instructions will be sent separately once the course materials are fully prepared for your learning journey. This ensures you receive a polished, error-free, and fully optimised experience from day one. Will This Work For Me? We’re Confident It Will.
Whether you're a cloud architect transitioning into AI systems, a solutions engineer scaling machine learning applications, or an IT leader modernising enterprise infrastructure, this course is built for real professionals with real goals. We’ve designed it to close the gap between foundational AWS knowledge and advanced AI-driven deployment excellence. Consider the experience of Maria L, Lead Systems Architect in Toronto: After completing the course, she redesigned her company’s entire inference pipeline, reducing latency by 40% and cutting monthly cloud spend by $18,000. Or James R, a DevOps engineer in Singapore, who used the security frameworks taught in Module 7 to pass his internal AWS audit on the first attempt and was promoted within six weeks. This works even if you’ve never built an AI production system before, if your current AWS knowledge is intermediate, or if you’re unsure how to align cloud architecture with machine learning workflows. The course is structured to build confidence step-by-step, with real examples, hands-on design templates, and practical decision frameworks that guide you from uncertainty to mastery. With lifetime access, expert support, a recognised certification, and a risk-free guarantee, the only real risk is not taking action. This is your moment to future-proof your skills and lead in the era of AI-driven enterprise transformation.
Extensive & Detailed Course Curriculum
Module 1: Foundations of AWS for AI-Powered Workloads - Introduction to cloud computing in the AI era
- Core AWS services and their role in enterprise AI systems
- Navigating the AWS Management Console and CLI
- Understanding AWS global infrastructure and regional design
- Identity and Access Management (IAM) for AI teams
- Setting up secure and scalable AWS accounts
- Best practices for multi-account AWS organisations
- Introduction to AI and machine learning workloads on AWS
- Data lifecycle management in cloud AI systems
- Fundamentals of network design for distributed AI components
- Introduction to AWS VPC and subnets configuration
- Security groups and network access control lists (NACLs)
- Using tags and resource groups for cost tracking
- Introduction to AWS pricing models and cost optimisation
- Overview of AI training versus inference resource needs
Module 2: Architectural Frameworks for AI-Ready Systems - Principles of AWS Well-Architected Framework
- Applying the five pillars to AI-driven architectures
- Designing for operational excellence in AI deployments
- Security best practices for AI data pipelines
- Reliability patterns for AI inference endpoints
- Performance efficiency in high-throughput training jobs
- Cost optimisation strategies for AI workloads
- Introduction to AWS Landing Zones
- Building scalable, modular architectures with microservices
- Event-driven architecture patterns using AWS EventBridge
- Understanding serverless computing in AI systems
- Decoupling AI components with message queues
- Using AWS Step Functions for orchestration
- Designing fault-tolerant AI inference workloads
- Multi-region deployment strategies for AI availability
Module 3: Data Architecture for AI and Machine Learning - Designing data lakes on Amazon S3 for AI
- Data ingestion strategies using AWS Transfer Family
- Streaming data with Amazon Kinesis
- Batch processing large datasets using AWS Glue
- Data cataloging and metadata management
- Partitioning and compression techniques for S3 performance
- Securing sensitive data in AI training pipelines
- Implementing data lineage and audit trails
- Using Amazon RDS and Aurora for metadata storage
- Building hybrid data architectures with on-premises systems
- Data transformation workflows using AWS Lambda
- Time-series data handling for predictive AI models
- Managing unstructured data with Amazon Rekognition and Textract
- Text and document processing pipelines
- Using Amazon DocumentDB for NoSQL AI data
Module 4: Machine Learning Infrastructure with SageMaker - Introduction to Amazon SageMaker and its components
- Setting up SageMaker notebooks securely
- Managing Jupyter environments for AI teams
- Data preparation workflows within SageMaker
- Custom Docker containers for model training
- Training machine learning models at scale
- Tuning hyperparameters with SageMaker Automatic Model Tuning
- Using built-in algorithms for classification and regression
- Deploying models to real-time inference endpoints
- Configuring scalable inference with SageMaker Endpoints
- Batch transform jobs for offline predictions
- Model monitoring and drift detection
- Explaining model predictions with SageMaker Clarify
- Securing SageMaker with IAM and networking controls
- Cost management for SageMaker training and inference
Module 5: AI and Deep Learning Specialisation - Using EC2 P3 and P4 instances for deep learning
- GPU optimisation techniques for training speed
- Setting up Amazon FSx for Lustre with SageMaker
- Distributed training across multiple GPUs
- Using Horovod for multi-node training
- Deep learning AMIs and their use cases
- Custom training scripts with PyTorch and TensorFlow
- Building custom inference containers
- Optimising inference latency with TensorRT
- Deploying models on edge devices using SageMaker Neo
- Natural language processing (NLP) architectures on AWS
- Computer vision pipelines with Amazon Rekognition
- Speech-to-text and text-to-speech with Amazon Transcribe and Polly
- Personalisation systems using Amazon Personalize
- Recommendation engine architectures
Module 6: Real-Time AI Processing and Streaming - Streaming data ingestion with Amazon Kinesis Data Streams
- Real-time processing with Kinesis Data Analytics
- Building serverless stream processors with Lambda
- Using Amazon Managed Streaming for Apache Kafka (MSK)
- Low-latency inference pipelines
- Event sourcing and CQRS patterns for AI
- Processing video streams for computer vision
- Real-time fraud detection architectures
- Streaming ETL for AI data preparation
- Reactive UI updates using Amazon API Gateway and WebSockets
- Temporal data handling in real-time AI systems
- Monitoring streaming pipelines with Amazon CloudWatch
- Fault tolerance in streaming AI architectures
- Scaling streaming consumers dynamically
- Building resilient event backlogs
Module 7: Security, Compliance, and Governance in AI Systems - Zero-trust security model for AI workloads
- Securing data in transit and at rest
- Using AWS KMS for encryption key management
- Implementing data masking and anonymisation
- Compliance frameworks for AI (GDPR, HIPAA, SOC 2)
- Audit logging with AWS CloudTrail
- Monitoring for unauthorised access with GuardDuty
- Securing SageMaker endpoints with VPC isolation
- PrivateLink for secure service connectivity
- Role-based access control in multi-user AI environments
- Best practices for secrets management using AWS Systems Manager Parameter Store
- Integration with HashiCorp Vault
- Penetration testing strategies for AI systems
- Incident response planning for AI infrastructure
- Security automation using AWS Security Hub
Module 8: DevOps and CI/CD for AI Applications - Infrastructure as Code with AWS CloudFormation
- Using AWS CDK for programmatic architecture
- Version controlling AI models with SageMaker Model Registry
- Building CI/CD pipelines with AWS CodePipeline
- Automated testing for AI services
- Blue/green deployments for SageMaker endpoints
- Canary releases for inference models
- Automated rollback mechanisms
- Integration testing for AI microservices
- Code reviews and pull request workflows
- Automating compliance checks in CI/CD
- Using Amazon ECR for container image management
- Secrets rotation in deployment pipelines
- Monitoring pipeline health and deployment success
- Scaling CI/CD for enterprise AI teams
Module 9: Observability, Monitoring, and Performance Tuning - Centralised logging with Amazon CloudWatch Logs
- Structured logging for AI applications
- Custom metrics for model performance and latency
- Alarm configuration for proactive issue detection
- Using AWS X-Ray for distributed tracing
- Analysing AI pipeline bottlenecks
- Performance benchmarking of inference endpoints
- Auto-scaling based on custom CloudWatch metrics
- Cost-aware scaling policies
- Monitoring GPU utilisation and memory
- Detecting model degradation over time
- Building custom dashboards for AI operations
- Proactive alerting using SNS and Lambda
- Log retention and archival strategies
- Root cause analysis for AI system failures
Module 10: Cost Optimisation and Financial Governance - Understanding AWS pricing for compute, storage, and data transfer
- Reserved Instances and Savings Plans for AI workloads
- Spot Instances for cost-effective training jobs
- Automating spot instance bidding strategies
- Cost allocation tags for AI projects
- Monitoring spending with AWS Cost Explorer
- Budget alerts and anomaly detection
- Right-sizing instances for AI tasks
- Comparing on-demand vs provisioned throughput
- Optimising S3 storage classes for AI data
- Automated archiving to Glacier Deep Archive
- Eliminating idle resources in development environments
- Multi-account cost reporting and chargebacks
- Using AWS Trusted Advisor for cost savings
- Implementing cost controls with Service Control Policies (SCPs)
Module 11: High Availability and Disaster Recovery - Designing for 99.99% availability in AI systems
- Data replication across AWS regions
- Automated backups using AWS Backup
- Point-in-time recovery for databases
- Cross-region failover strategies
- DNS failover with Amazon Route 53
- Testing disaster recovery plans
- Automated recovery workflows
- Ensuring data consistency during failover
- Recovery Time Objective (RTO) and Recovery Point Objective (RPO) planning
- Active-active versus active-passive architectures
- Using AWS Global Accelerator for performance and resilience
- Monitoring global health of AI services
- Documentation and runbooks for incident response
- Compliance requirements for data replication
Module 12: Edge AI and IoT Integrations - Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
Module 1: Foundations of AWS for AI-Powered Workloads - Introduction to cloud computing in the AI era
- Core AWS services and their role in enterprise AI systems
- Navigating the AWS Management Console and CLI
- Understanding AWS global infrastructure and regional design
- Identity and Access Management (IAM) for AI teams
- Setting up secure and scalable AWS accounts
- Best practices for multi-account AWS organisations
- Introduction to AI and machine learning workloads on AWS
- Data lifecycle management in cloud AI systems
- Fundamentals of network design for distributed AI components
- Introduction to AWS VPC and subnets configuration
- Security groups and network access control lists (NACLs)
- Using tags and resource groups for cost tracking
- Introduction to AWS pricing models and cost optimisation
- Overview of AI training versus inference resource needs
Module 2: Architectural Frameworks for AI-Ready Systems - Principles of AWS Well-Architected Framework
- Applying the five pillars to AI-driven architectures
- Designing for operational excellence in AI deployments
- Security best practices for AI data pipelines
- Reliability patterns for AI inference endpoints
- Performance efficiency in high-throughput training jobs
- Cost optimisation strategies for AI workloads
- Introduction to AWS Landing Zones
- Building scalable, modular architectures with microservices
- Event-driven architecture patterns using AWS EventBridge
- Understanding serverless computing in AI systems
- Decoupling AI components with message queues
- Using AWS Step Functions for orchestration
- Designing fault-tolerant AI inference workloads
- Multi-region deployment strategies for AI availability
Module 3: Data Architecture for AI and Machine Learning - Designing data lakes on Amazon S3 for AI
- Data ingestion strategies using AWS Transfer Family
- Streaming data with Amazon Kinesis
- Batch processing large datasets using AWS Glue
- Data cataloging and metadata management
- Partitioning and compression techniques for S3 performance
- Securing sensitive data in AI training pipelines
- Implementing data lineage and audit trails
- Using Amazon RDS and Aurora for metadata storage
- Building hybrid data architectures with on-premises systems
- Data transformation workflows using AWS Lambda
- Time-series data handling for predictive AI models
- Managing unstructured data with Amazon Rekognition and Textract
- Text and document processing pipelines
- Using Amazon DocumentDB for NoSQL AI data
Module 4: Machine Learning Infrastructure with SageMaker - Introduction to Amazon SageMaker and its components
- Setting up SageMaker notebooks securely
- Managing Jupyter environments for AI teams
- Data preparation workflows within SageMaker
- Custom Docker containers for model training
- Training machine learning models at scale
- Tuning hyperparameters with SageMaker Automatic Model Tuning
- Using built-in algorithms for classification and regression
- Deploying models to real-time inference endpoints
- Configuring scalable inference with SageMaker Endpoints
- Batch transform jobs for offline predictions
- Model monitoring and drift detection
- Explaining model predictions with SageMaker Clarify
- Securing SageMaker with IAM and networking controls
- Cost management for SageMaker training and inference
Module 5: AI and Deep Learning Specialisation - Using EC2 P3 and P4 instances for deep learning
- GPU optimisation techniques for training speed
- Setting up Amazon FSx for Lustre with SageMaker
- Distributed training across multiple GPUs
- Using Horovod for multi-node training
- Deep learning AMIs and their use cases
- Custom training scripts with PyTorch and TensorFlow
- Building custom inference containers
- Optimising inference latency with TensorRT
- Deploying models on edge devices using SageMaker Neo
- Natural language processing (NLP) architectures on AWS
- Computer vision pipelines with Amazon Rekognition
- Speech-to-text and text-to-speech with Amazon Transcribe and Polly
- Personalisation systems using Amazon Personalize
- Recommendation engine architectures
Module 6: Real-Time AI Processing and Streaming - Streaming data ingestion with Amazon Kinesis Data Streams
- Real-time processing with Kinesis Data Analytics
- Building serverless stream processors with Lambda
- Using Amazon Managed Streaming for Apache Kafka (MSK)
- Low-latency inference pipelines
- Event sourcing and CQRS patterns for AI
- Processing video streams for computer vision
- Real-time fraud detection architectures
- Streaming ETL for AI data preparation
- Reactive UI updates using Amazon API Gateway and WebSockets
- Temporal data handling in real-time AI systems
- Monitoring streaming pipelines with Amazon CloudWatch
- Fault tolerance in streaming AI architectures
- Scaling streaming consumers dynamically
- Building resilient event backlogs
Module 7: Security, Compliance, and Governance in AI Systems - Zero-trust security model for AI workloads
- Securing data in transit and at rest
- Using AWS KMS for encryption key management
- Implementing data masking and anonymisation
- Compliance frameworks for AI (GDPR, HIPAA, SOC 2)
- Audit logging with AWS CloudTrail
- Monitoring for unauthorised access with GuardDuty
- Securing SageMaker endpoints with VPC isolation
- PrivateLink for secure service connectivity
- Role-based access control in multi-user AI environments
- Best practices for secrets management using AWS Systems Manager Parameter Store
- Integration with HashiCorp Vault
- Penetration testing strategies for AI systems
- Incident response planning for AI infrastructure
- Security automation using AWS Security Hub
Module 8: DevOps and CI/CD for AI Applications - Infrastructure as Code with AWS CloudFormation
- Using AWS CDK for programmatic architecture
- Version controlling AI models with SageMaker Model Registry
- Building CI/CD pipelines with AWS CodePipeline
- Automated testing for AI services
- Blue/green deployments for SageMaker endpoints
- Canary releases for inference models
- Automated rollback mechanisms
- Integration testing for AI microservices
- Code reviews and pull request workflows
- Automating compliance checks in CI/CD
- Using Amazon ECR for container image management
- Secrets rotation in deployment pipelines
- Monitoring pipeline health and deployment success
- Scaling CI/CD for enterprise AI teams
Module 9: Observability, Monitoring, and Performance Tuning - Centralised logging with Amazon CloudWatch Logs
- Structured logging for AI applications
- Custom metrics for model performance and latency
- Alarm configuration for proactive issue detection
- Using AWS X-Ray for distributed tracing
- Analysing AI pipeline bottlenecks
- Performance benchmarking of inference endpoints
- Auto-scaling based on custom CloudWatch metrics
- Cost-aware scaling policies
- Monitoring GPU utilisation and memory
- Detecting model degradation over time
- Building custom dashboards for AI operations
- Proactive alerting using SNS and Lambda
- Log retention and archival strategies
- Root cause analysis for AI system failures
Module 10: Cost Optimisation and Financial Governance - Understanding AWS pricing for compute, storage, and data transfer
- Reserved Instances and Savings Plans for AI workloads
- Spot Instances for cost-effective training jobs
- Automating spot instance bidding strategies
- Cost allocation tags for AI projects
- Monitoring spending with AWS Cost Explorer
- Budget alerts and anomaly detection
- Right-sizing instances for AI tasks
- Comparing on-demand vs provisioned throughput
- Optimising S3 storage classes for AI data
- Automated archiving to Glacier Deep Archive
- Eliminating idle resources in development environments
- Multi-account cost reporting and chargebacks
- Using AWS Trusted Advisor for cost savings
- Implementing cost controls with Service Control Policies (SCPs)
Module 11: High Availability and Disaster Recovery - Designing for 99.99% availability in AI systems
- Data replication across AWS regions
- Automated backups using AWS Backup
- Point-in-time recovery for databases
- Cross-region failover strategies
- DNS failover with Amazon Route 53
- Testing disaster recovery plans
- Automated recovery workflows
- Ensuring data consistency during failover
- Recovery Time Objective (RTO) and Recovery Point Objective (RPO) planning
- Active-active versus active-passive architectures
- Using AWS Global Accelerator for performance and resilience
- Monitoring global health of AI services
- Documentation and runbooks for incident response
- Compliance requirements for data replication
Module 12: Edge AI and IoT Integrations - Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
- Principles of AWS Well-Architected Framework
- Applying the five pillars to AI-driven architectures
- Designing for operational excellence in AI deployments
- Security best practices for AI data pipelines
- Reliability patterns for AI inference endpoints
- Performance efficiency in high-throughput training jobs
- Cost optimisation strategies for AI workloads
- Introduction to AWS Landing Zones
- Building scalable, modular architectures with microservices
- Event-driven architecture patterns using AWS EventBridge
- Understanding serverless computing in AI systems
- Decoupling AI components with message queues
- Using AWS Step Functions for orchestration
- Designing fault-tolerant AI inference workloads
- Multi-region deployment strategies for AI availability
Module 3: Data Architecture for AI and Machine Learning - Designing data lakes on Amazon S3 for AI
- Data ingestion strategies using AWS Transfer Family
- Streaming data with Amazon Kinesis
- Batch processing large datasets using AWS Glue
- Data cataloging and metadata management
- Partitioning and compression techniques for S3 performance
- Securing sensitive data in AI training pipelines
- Implementing data lineage and audit trails
- Using Amazon RDS and Aurora for metadata storage
- Building hybrid data architectures with on-premises systems
- Data transformation workflows using AWS Lambda
- Time-series data handling for predictive AI models
- Managing unstructured data with Amazon Rekognition and Textract
- Text and document processing pipelines
- Using Amazon DocumentDB for NoSQL AI data
Module 4: Machine Learning Infrastructure with SageMaker - Introduction to Amazon SageMaker and its components
- Setting up SageMaker notebooks securely
- Managing Jupyter environments for AI teams
- Data preparation workflows within SageMaker
- Custom Docker containers for model training
- Training machine learning models at scale
- Tuning hyperparameters with SageMaker Automatic Model Tuning
- Using built-in algorithms for classification and regression
- Deploying models to real-time inference endpoints
- Configuring scalable inference with SageMaker Endpoints
- Batch transform jobs for offline predictions
- Model monitoring and drift detection
- Explaining model predictions with SageMaker Clarify
- Securing SageMaker with IAM and networking controls
- Cost management for SageMaker training and inference
Module 5: AI and Deep Learning Specialisation - Using EC2 P3 and P4 instances for deep learning
- GPU optimisation techniques for training speed
- Setting up Amazon FSx for Lustre with SageMaker
- Distributed training across multiple GPUs
- Using Horovod for multi-node training
- Deep learning AMIs and their use cases
- Custom training scripts with PyTorch and TensorFlow
- Building custom inference containers
- Optimising inference latency with TensorRT
- Deploying models on edge devices using SageMaker Neo
- Natural language processing (NLP) architectures on AWS
- Computer vision pipelines with Amazon Rekognition
- Speech-to-text and text-to-speech with Amazon Transcribe and Polly
- Personalisation systems using Amazon Personalize
- Recommendation engine architectures
Module 6: Real-Time AI Processing and Streaming - Streaming data ingestion with Amazon Kinesis Data Streams
- Real-time processing with Kinesis Data Analytics
- Building serverless stream processors with Lambda
- Using Amazon Managed Streaming for Apache Kafka (MSK)
- Low-latency inference pipelines
- Event sourcing and CQRS patterns for AI
- Processing video streams for computer vision
- Real-time fraud detection architectures
- Streaming ETL for AI data preparation
- Reactive UI updates using Amazon API Gateway and WebSockets
- Temporal data handling in real-time AI systems
- Monitoring streaming pipelines with Amazon CloudWatch
- Fault tolerance in streaming AI architectures
- Scaling streaming consumers dynamically
- Building resilient event backlogs
Module 7: Security, Compliance, and Governance in AI Systems - Zero-trust security model for AI workloads
- Securing data in transit and at rest
- Using AWS KMS for encryption key management
- Implementing data masking and anonymisation
- Compliance frameworks for AI (GDPR, HIPAA, SOC 2)
- Audit logging with AWS CloudTrail
- Monitoring for unauthorised access with GuardDuty
- Securing SageMaker endpoints with VPC isolation
- PrivateLink for secure service connectivity
- Role-based access control in multi-user AI environments
- Best practices for secrets management using AWS Systems Manager Parameter Store
- Integration with HashiCorp Vault
- Penetration testing strategies for AI systems
- Incident response planning for AI infrastructure
- Security automation using AWS Security Hub
Module 8: DevOps and CI/CD for AI Applications - Infrastructure as Code with AWS CloudFormation
- Using AWS CDK for programmatic architecture
- Version controlling AI models with SageMaker Model Registry
- Building CI/CD pipelines with AWS CodePipeline
- Automated testing for AI services
- Blue/green deployments for SageMaker endpoints
- Canary releases for inference models
- Automated rollback mechanisms
- Integration testing for AI microservices
- Code reviews and pull request workflows
- Automating compliance checks in CI/CD
- Using Amazon ECR for container image management
- Secrets rotation in deployment pipelines
- Monitoring pipeline health and deployment success
- Scaling CI/CD for enterprise AI teams
Module 9: Observability, Monitoring, and Performance Tuning - Centralised logging with Amazon CloudWatch Logs
- Structured logging for AI applications
- Custom metrics for model performance and latency
- Alarm configuration for proactive issue detection
- Using AWS X-Ray for distributed tracing
- Analysing AI pipeline bottlenecks
- Performance benchmarking of inference endpoints
- Auto-scaling based on custom CloudWatch metrics
- Cost-aware scaling policies
- Monitoring GPU utilisation and memory
- Detecting model degradation over time
- Building custom dashboards for AI operations
- Proactive alerting using SNS and Lambda
- Log retention and archival strategies
- Root cause analysis for AI system failures
Module 10: Cost Optimisation and Financial Governance - Understanding AWS pricing for compute, storage, and data transfer
- Reserved Instances and Savings Plans for AI workloads
- Spot Instances for cost-effective training jobs
- Automating spot instance bidding strategies
- Cost allocation tags for AI projects
- Monitoring spending with AWS Cost Explorer
- Budget alerts and anomaly detection
- Right-sizing instances for AI tasks
- Comparing on-demand vs provisioned throughput
- Optimising S3 storage classes for AI data
- Automated archiving to Glacier Deep Archive
- Eliminating idle resources in development environments
- Multi-account cost reporting and chargebacks
- Using AWS Trusted Advisor for cost savings
- Implementing cost controls with Service Control Policies (SCPs)
Module 11: High Availability and Disaster Recovery - Designing for 99.99% availability in AI systems
- Data replication across AWS regions
- Automated backups using AWS Backup
- Point-in-time recovery for databases
- Cross-region failover strategies
- DNS failover with Amazon Route 53
- Testing disaster recovery plans
- Automated recovery workflows
- Ensuring data consistency during failover
- Recovery Time Objective (RTO) and Recovery Point Objective (RPO) planning
- Active-active versus active-passive architectures
- Using AWS Global Accelerator for performance and resilience
- Monitoring global health of AI services
- Documentation and runbooks for incident response
- Compliance requirements for data replication
Module 12: Edge AI and IoT Integrations - Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
- Introduction to Amazon SageMaker and its components
- Setting up SageMaker notebooks securely
- Managing Jupyter environments for AI teams
- Data preparation workflows within SageMaker
- Custom Docker containers for model training
- Training machine learning models at scale
- Tuning hyperparameters with SageMaker Automatic Model Tuning
- Using built-in algorithms for classification and regression
- Deploying models to real-time inference endpoints
- Configuring scalable inference with SageMaker Endpoints
- Batch transform jobs for offline predictions
- Model monitoring and drift detection
- Explaining model predictions with SageMaker Clarify
- Securing SageMaker with IAM and networking controls
- Cost management for SageMaker training and inference
Module 5: AI and Deep Learning Specialisation - Using EC2 P3 and P4 instances for deep learning
- GPU optimisation techniques for training speed
- Setting up Amazon FSx for Lustre with SageMaker
- Distributed training across multiple GPUs
- Using Horovod for multi-node training
- Deep learning AMIs and their use cases
- Custom training scripts with PyTorch and TensorFlow
- Building custom inference containers
- Optimising inference latency with TensorRT
- Deploying models on edge devices using SageMaker Neo
- Natural language processing (NLP) architectures on AWS
- Computer vision pipelines with Amazon Rekognition
- Speech-to-text and text-to-speech with Amazon Transcribe and Polly
- Personalisation systems using Amazon Personalize
- Recommendation engine architectures
Module 6: Real-Time AI Processing and Streaming - Streaming data ingestion with Amazon Kinesis Data Streams
- Real-time processing with Kinesis Data Analytics
- Building serverless stream processors with Lambda
- Using Amazon Managed Streaming for Apache Kafka (MSK)
- Low-latency inference pipelines
- Event sourcing and CQRS patterns for AI
- Processing video streams for computer vision
- Real-time fraud detection architectures
- Streaming ETL for AI data preparation
- Reactive UI updates using Amazon API Gateway and WebSockets
- Temporal data handling in real-time AI systems
- Monitoring streaming pipelines with Amazon CloudWatch
- Fault tolerance in streaming AI architectures
- Scaling streaming consumers dynamically
- Building resilient event backlogs
Module 7: Security, Compliance, and Governance in AI Systems - Zero-trust security model for AI workloads
- Securing data in transit and at rest
- Using AWS KMS for encryption key management
- Implementing data masking and anonymisation
- Compliance frameworks for AI (GDPR, HIPAA, SOC 2)
- Audit logging with AWS CloudTrail
- Monitoring for unauthorised access with GuardDuty
- Securing SageMaker endpoints with VPC isolation
- PrivateLink for secure service connectivity
- Role-based access control in multi-user AI environments
- Best practices for secrets management using AWS Systems Manager Parameter Store
- Integration with HashiCorp Vault
- Penetration testing strategies for AI systems
- Incident response planning for AI infrastructure
- Security automation using AWS Security Hub
Module 8: DevOps and CI/CD for AI Applications - Infrastructure as Code with AWS CloudFormation
- Using AWS CDK for programmatic architecture
- Version controlling AI models with SageMaker Model Registry
- Building CI/CD pipelines with AWS CodePipeline
- Automated testing for AI services
- Blue/green deployments for SageMaker endpoints
- Canary releases for inference models
- Automated rollback mechanisms
- Integration testing for AI microservices
- Code reviews and pull request workflows
- Automating compliance checks in CI/CD
- Using Amazon ECR for container image management
- Secrets rotation in deployment pipelines
- Monitoring pipeline health and deployment success
- Scaling CI/CD for enterprise AI teams
Module 9: Observability, Monitoring, and Performance Tuning - Centralised logging with Amazon CloudWatch Logs
- Structured logging for AI applications
- Custom metrics for model performance and latency
- Alarm configuration for proactive issue detection
- Using AWS X-Ray for distributed tracing
- Analysing AI pipeline bottlenecks
- Performance benchmarking of inference endpoints
- Auto-scaling based on custom CloudWatch metrics
- Cost-aware scaling policies
- Monitoring GPU utilisation and memory
- Detecting model degradation over time
- Building custom dashboards for AI operations
- Proactive alerting using SNS and Lambda
- Log retention and archival strategies
- Root cause analysis for AI system failures
Module 10: Cost Optimisation and Financial Governance - Understanding AWS pricing for compute, storage, and data transfer
- Reserved Instances and Savings Plans for AI workloads
- Spot Instances for cost-effective training jobs
- Automating spot instance bidding strategies
- Cost allocation tags for AI projects
- Monitoring spending with AWS Cost Explorer
- Budget alerts and anomaly detection
- Right-sizing instances for AI tasks
- Comparing on-demand vs provisioned throughput
- Optimising S3 storage classes for AI data
- Automated archiving to Glacier Deep Archive
- Eliminating idle resources in development environments
- Multi-account cost reporting and chargebacks
- Using AWS Trusted Advisor for cost savings
- Implementing cost controls with Service Control Policies (SCPs)
Module 11: High Availability and Disaster Recovery - Designing for 99.99% availability in AI systems
- Data replication across AWS regions
- Automated backups using AWS Backup
- Point-in-time recovery for databases
- Cross-region failover strategies
- DNS failover with Amazon Route 53
- Testing disaster recovery plans
- Automated recovery workflows
- Ensuring data consistency during failover
- Recovery Time Objective (RTO) and Recovery Point Objective (RPO) planning
- Active-active versus active-passive architectures
- Using AWS Global Accelerator for performance and resilience
- Monitoring global health of AI services
- Documentation and runbooks for incident response
- Compliance requirements for data replication
Module 12: Edge AI and IoT Integrations - Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
- Streaming data ingestion with Amazon Kinesis Data Streams
- Real-time processing with Kinesis Data Analytics
- Building serverless stream processors with Lambda
- Using Amazon Managed Streaming for Apache Kafka (MSK)
- Low-latency inference pipelines
- Event sourcing and CQRS patterns for AI
- Processing video streams for computer vision
- Real-time fraud detection architectures
- Streaming ETL for AI data preparation
- Reactive UI updates using Amazon API Gateway and WebSockets
- Temporal data handling in real-time AI systems
- Monitoring streaming pipelines with Amazon CloudWatch
- Fault tolerance in streaming AI architectures
- Scaling streaming consumers dynamically
- Building resilient event backlogs
Module 7: Security, Compliance, and Governance in AI Systems - Zero-trust security model for AI workloads
- Securing data in transit and at rest
- Using AWS KMS for encryption key management
- Implementing data masking and anonymisation
- Compliance frameworks for AI (GDPR, HIPAA, SOC 2)
- Audit logging with AWS CloudTrail
- Monitoring for unauthorised access with GuardDuty
- Securing SageMaker endpoints with VPC isolation
- PrivateLink for secure service connectivity
- Role-based access control in multi-user AI environments
- Best practices for secrets management using AWS Systems Manager Parameter Store
- Integration with HashiCorp Vault
- Penetration testing strategies for AI systems
- Incident response planning for AI infrastructure
- Security automation using AWS Security Hub
Module 8: DevOps and CI/CD for AI Applications - Infrastructure as Code with AWS CloudFormation
- Using AWS CDK for programmatic architecture
- Version controlling AI models with SageMaker Model Registry
- Building CI/CD pipelines with AWS CodePipeline
- Automated testing for AI services
- Blue/green deployments for SageMaker endpoints
- Canary releases for inference models
- Automated rollback mechanisms
- Integration testing for AI microservices
- Code reviews and pull request workflows
- Automating compliance checks in CI/CD
- Using Amazon ECR for container image management
- Secrets rotation in deployment pipelines
- Monitoring pipeline health and deployment success
- Scaling CI/CD for enterprise AI teams
Module 9: Observability, Monitoring, and Performance Tuning - Centralised logging with Amazon CloudWatch Logs
- Structured logging for AI applications
- Custom metrics for model performance and latency
- Alarm configuration for proactive issue detection
- Using AWS X-Ray for distributed tracing
- Analysing AI pipeline bottlenecks
- Performance benchmarking of inference endpoints
- Auto-scaling based on custom CloudWatch metrics
- Cost-aware scaling policies
- Monitoring GPU utilisation and memory
- Detecting model degradation over time
- Building custom dashboards for AI operations
- Proactive alerting using SNS and Lambda
- Log retention and archival strategies
- Root cause analysis for AI system failures
Module 10: Cost Optimisation and Financial Governance - Understanding AWS pricing for compute, storage, and data transfer
- Reserved Instances and Savings Plans for AI workloads
- Spot Instances for cost-effective training jobs
- Automating spot instance bidding strategies
- Cost allocation tags for AI projects
- Monitoring spending with AWS Cost Explorer
- Budget alerts and anomaly detection
- Right-sizing instances for AI tasks
- Comparing on-demand vs provisioned throughput
- Optimising S3 storage classes for AI data
- Automated archiving to Glacier Deep Archive
- Eliminating idle resources in development environments
- Multi-account cost reporting and chargebacks
- Using AWS Trusted Advisor for cost savings
- Implementing cost controls with Service Control Policies (SCPs)
Module 11: High Availability and Disaster Recovery - Designing for 99.99% availability in AI systems
- Data replication across AWS regions
- Automated backups using AWS Backup
- Point-in-time recovery for databases
- Cross-region failover strategies
- DNS failover with Amazon Route 53
- Testing disaster recovery plans
- Automated recovery workflows
- Ensuring data consistency during failover
- Recovery Time Objective (RTO) and Recovery Point Objective (RPO) planning
- Active-active versus active-passive architectures
- Using AWS Global Accelerator for performance and resilience
- Monitoring global health of AI services
- Documentation and runbooks for incident response
- Compliance requirements for data replication
Module 12: Edge AI and IoT Integrations - Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
- Infrastructure as Code with AWS CloudFormation
- Using AWS CDK for programmatic architecture
- Version controlling AI models with SageMaker Model Registry
- Building CI/CD pipelines with AWS CodePipeline
- Automated testing for AI services
- Blue/green deployments for SageMaker endpoints
- Canary releases for inference models
- Automated rollback mechanisms
- Integration testing for AI microservices
- Code reviews and pull request workflows
- Automating compliance checks in CI/CD
- Using Amazon ECR for container image management
- Secrets rotation in deployment pipelines
- Monitoring pipeline health and deployment success
- Scaling CI/CD for enterprise AI teams
Module 9: Observability, Monitoring, and Performance Tuning - Centralised logging with Amazon CloudWatch Logs
- Structured logging for AI applications
- Custom metrics for model performance and latency
- Alarm configuration for proactive issue detection
- Using AWS X-Ray for distributed tracing
- Analysing AI pipeline bottlenecks
- Performance benchmarking of inference endpoints
- Auto-scaling based on custom CloudWatch metrics
- Cost-aware scaling policies
- Monitoring GPU utilisation and memory
- Detecting model degradation over time
- Building custom dashboards for AI operations
- Proactive alerting using SNS and Lambda
- Log retention and archival strategies
- Root cause analysis for AI system failures
Module 10: Cost Optimisation and Financial Governance - Understanding AWS pricing for compute, storage, and data transfer
- Reserved Instances and Savings Plans for AI workloads
- Spot Instances for cost-effective training jobs
- Automating spot instance bidding strategies
- Cost allocation tags for AI projects
- Monitoring spending with AWS Cost Explorer
- Budget alerts and anomaly detection
- Right-sizing instances for AI tasks
- Comparing on-demand vs provisioned throughput
- Optimising S3 storage classes for AI data
- Automated archiving to Glacier Deep Archive
- Eliminating idle resources in development environments
- Multi-account cost reporting and chargebacks
- Using AWS Trusted Advisor for cost savings
- Implementing cost controls with Service Control Policies (SCPs)
Module 11: High Availability and Disaster Recovery - Designing for 99.99% availability in AI systems
- Data replication across AWS regions
- Automated backups using AWS Backup
- Point-in-time recovery for databases
- Cross-region failover strategies
- DNS failover with Amazon Route 53
- Testing disaster recovery plans
- Automated recovery workflows
- Ensuring data consistency during failover
- Recovery Time Objective (RTO) and Recovery Point Objective (RPO) planning
- Active-active versus active-passive architectures
- Using AWS Global Accelerator for performance and resilience
- Monitoring global health of AI services
- Documentation and runbooks for incident response
- Compliance requirements for data replication
Module 12: Edge AI and IoT Integrations - Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
- Understanding AWS pricing for compute, storage, and data transfer
- Reserved Instances and Savings Plans for AI workloads
- Spot Instances for cost-effective training jobs
- Automating spot instance bidding strategies
- Cost allocation tags for AI projects
- Monitoring spending with AWS Cost Explorer
- Budget alerts and anomaly detection
- Right-sizing instances for AI tasks
- Comparing on-demand vs provisioned throughput
- Optimising S3 storage classes for AI data
- Automated archiving to Glacier Deep Archive
- Eliminating idle resources in development environments
- Multi-account cost reporting and chargebacks
- Using AWS Trusted Advisor for cost savings
- Implementing cost controls with Service Control Policies (SCPs)
Module 11: High Availability and Disaster Recovery - Designing for 99.99% availability in AI systems
- Data replication across AWS regions
- Automated backups using AWS Backup
- Point-in-time recovery for databases
- Cross-region failover strategies
- DNS failover with Amazon Route 53
- Testing disaster recovery plans
- Automated recovery workflows
- Ensuring data consistency during failover
- Recovery Time Objective (RTO) and Recovery Point Objective (RPO) planning
- Active-active versus active-passive architectures
- Using AWS Global Accelerator for performance and resilience
- Monitoring global health of AI services
- Documentation and runbooks for incident response
- Compliance requirements for data replication
Module 12: Edge AI and IoT Integrations - Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
- Bringing AI inference to edge devices
- Using AWS IoT Greengrass for local processing
- Deploying models to IoT devices securely
- Over-the-air (OTA) model updates
- Processing sensor data with AWS IoT Rules
- Time-series data handling with Amazon Timestream
- Low-latency AI inference at the edge
- Offline AI capabilities for remote locations
- Security of edge devices and AI models
- Monitoring device health and AI performance
- Batching edge data for cloud retraining
- Energy-efficient AI inference
- Use cases in manufacturing, logistics, and healthcare
- Combining computer vision with IoT sensors
- Scalability of edge AI deployments
Module 13: AI Integration with Enterprise Systems - Integrating AI services with legacy applications
- Building APIs with Amazon API Gateway
- Securing APIs with JWT and OAuth
- Throttling and rate limiting for AI endpoints
- Using AWS AppSync for GraphQL APIs
- Event integration with enterprise messaging systems
- Connecting AI models to ERP and CRM platforms
- Automating business processes with AI insights
- Building chatbots using Amazon Lex
- Integrating AI into customer service workflows
- Data synchronisation between cloud and on-premises
- Hybrid AI architectures with AWS Outposts
- Single sign-on with AWS SSO
- Directory integration using AWS Directory Service
- API versioning and lifecycle management
Module 14: Hands-On AI Architecture Projects - Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates
Module 15: Certification Preparation and Career Advancement - Mapping course content to AWS certification paths
- Preparation for AWS Solutions Architect – Professional
- Understanding AI-specific exam topics
- Architecture decision patterns in certification exams
- Time management and question analysis strategies
- Case study breakdown techniques
- Sample scenario-based questions and responses
- Creating a personal study roadmap
- Leveraging the Certificate of Completion in job applications
- Updating your LinkedIn and resume with new credentials
- Presenting architecture diagrams to hiring managers
- Building a personal AI architecture portfolio
- Freelancing and consulting opportunities with AWS AI skills
- Negotiating higher salaries with verified expertise
- Next steps: advanced certifications and specialisations
- Designing an end-to-end fraud detection system
- Building a real-time sentiment analysis pipeline
- Creating a scalable document processing workflow
- Deploying a recommendation engine for e-commerce
- Architecting a video analytics platform
- Designing a predictive maintenance system for IoT
- Building a speech-driven customer portal
- Implementing a multi-modal AI assistant
- Creating a serverless data lake for AI
- Designing a private AI model marketplace
- Securing AI access in a regulated industry
- Cost-optimised training architecture for large models
- High-availability inference cluster design
- Disaster recovery plan for AI endpoints
- CI/CD pipeline for zero-downtime model updates