AI Infrastructure on AWS
Published by Pearson
From Cloud-Native AI Services to Production Pipelines
- Deep AWS AI Ecosystem Mastery: Gain comprehensive coverage of the entire AWS AI/ML stack including SageMaker, Bedrock, Comprehend, and Rekognition with hands-on experience across all major services.
- Production-Ready Cost Optimization: Learn advanced AWS-specific techniques including SageMaker Savings Plans, Spot Training, and Serverless Inference to reduce ML infrastructure costs by 50-80%.
- End-to-End MLOps Implementation: Build complete production pipelines using SageMaker Pipelines, CodePipeline, and EventBridge for automated model training, deployment, and monitoring.
This intensive 4-hour course is designed for AWS practitioners, ML engineers, and cloud architects who need to rapidly deploy production-ready AI infrastructure on Amazon Web Services. You will gain deep expertise with AWS SageMaker ecosystem, learn to leverage AWS-native AI services, and master cost optimization strategies that make AWS AI deployments financially sustainable at scale.
The course combines AWS best practices with hands-on implementation, where you'll build actual end-to-end ML pipelines using SageMaker Studio, deploy models with auto-scaling inference endpoints, and integrate with AWS data services such as S3, Glue, and Athena. You will also explore AWS's managed AI services for common use cases and learn to architect secure, compliant AI solutions using IAM, VPC, and AWS security frameworks. By the end of this training, you will be equipped to design and implement enterprise-grade AI infrastructure that leverages AWS's full AI/ML capabilities.
What you’ll learn and how you can apply it
- Architect complete AWS AI solutions by designing end-to-end machine learning infrastructure using SageMaker, Lambda, API Gateway, and other core AWS services.
- Deploy and manage production-grade ML pipelines using SageMaker Pipelines with advanced features such as model registry, A/B testing, and automated rollbacks.
- Integrate AWS-native AI services such as Bedrock, Comprehend, Textract, and Rekognition to accelerate development of NLP, computer vision, and generative AI applications.
- Optimize and secure AI workloads through intelligent cost-saving strategies, IAM configuration, VPC networking, and scalable monitoring with CloudWatch and auto-scaling policies.
This live event is for you because...
- You are a network engineer, architect, or operations specialist who recognizes the growing impact of AI on networking technologies and wants to stay ahead of industry trends.
- You aim to enhance your organization's network operations with AI capabilities or advance your career by developing cross-functional expertise.
Prerequisites
- AWS fundamentals: Basic understanding of core AWS services including EC2, S3, IAM, and VPC concepts
- Command line proficiency: Comfortable with AWS CLI, terminal operations, and basic shell scripting
- JSON/YAML familiarity: Able to read and modify configuration files and CloudFormation templates
Course Set-up
- Attendees will need an AWS account. All additional resources will be provided through AWS, and we will walk through how to access them during the session.
Recommended Preparation
- Attend: Generative AI for Cloud Practitioners by Chad Smith
Recommended Follow-up
- Attend Generative AI for AWS Operations by Chad Smith
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: AWS AI Ecosystem and SageMaker Fundamentals (70 min)
- AWS AI/ML services landscape and use case mapping
- SageMaker Studio setup and notebook environments
- EC2 instance types for ML: P4, G5, Trn1, and cost considerations
- VPC configuration and security best practices for ML workloads
Q&A (10 Minutes)
Break (5 minutes)
Segment 2: Production ML Pipelines with SageMaker (60 min)
- SageMaker Training Jobs with custom containers and algorithms
- Model Registry, versioning, and approval workflows
- SageMaker Pipelines for automated MLOps
- Real-time and batch inference deployment strategies
Q&A (10 Minutes)
Break (5 minutes)
Segment 3: Cost Optimization and Production Monitoring (70 min)
- SageMaker Savings Plans and Spot Training strategies
- CloudWatch monitoring, logging, and alerting for ML workloads
- Performance tuning and auto-scaling configurations
Q&A (10 minutes)
Your Instructor
Vinit Jain
Vinit Jain, CCIE No. 22854 (R&S, SP, Security & DC), is a CTO / Principal Engineer at Iraitech Innovations & Technologies Pvt. Ltd. Prior to that, he was working as a Sr. Technical Leader for Network Engineering at Cisco focusing on architecting network infrastructure for edge computing solutions. He also worked as a Network Development Engineer at Amazon as part of Amazon’s backbone network operations team and as a technical leader at Cisco Technical Assistance Center (TAC), providing escalation support in routing and data center technologies. Vinit is a speaker at various networking forums, including Cisco Live! events. He has co-authored several Cisco Press books, Apress Books, and video courses with Cisco Press. In addition to his CCIEs, Vinit holds multiple certifications related to programming and databases, and he is also a CEH. Vinit graduated from Delhi University in mathematics and earned a master's in information technology from Kuvempu University in India. Visit Vinit's youtube channel to learn more.