AI in the Cloud

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Course Overview This course delves into the powerful synergy of artificial intelligence and cloud computing, demonstrating how leading cloud providers—AWS, Azure, and Google Cloud —enable the scalable, accessible, and secure development and deployment of advanced AI solutions. Students will master leveraging cloud services for efficient petabyte-scale data processing, robust distributed model training, and seamless hosting of real-time AI applications. A core focus will be on optimizing performance, achieving cost-efficiency through FinOps principles, and ensuring enterprise-grade security and compliance.

Evaluate and select optimal cloud services across AWS, Azure, and Google Cloud for diverse AI workloads, balancing performance and cost effectiveness.
Design and implement resilient, fault-tolerant cloudnative architectures for a wide range of AI applications, from machine learning to generative AI.
Construct high-throughput data pipelines using cloud-native tools (e.g., AWS Glue, Azure Data Factory, Google Cloud Dataflow) and orchestrate distributed model training on large datasets.
Deploy and manage AI models for both real-time inference and batch processing using services like Amazon SageMaker Endpoints, Azure Machine Learning Endpoints, and Google Cloud Vertex AI Endpoints.
Formulate and execute advanced cost optimization strategies, including right-sizing, Spot Instances, reserved instances, and serverless computing for AI workloads.
Implement robust security measures (e.g., IAM roles, VPCs, data encryption) and ensure compliance with industry regulations (e.g., GDPR, HIPAA) for cloudbased AI solutions.

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Cloud Platforms for AI
This module introduces core cloud computing concepts (IaaS, PaaS, SaaS) and provides a deep dive into AI service portfolios across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). It offers a comparative analysis of managed AI/ML services (e.g., SageMaker, Azure ML, Vertex AI) versus custom deployments. Students will explore cloud economics specific to AI applications, including cost models for compute, storage, and specialized hardware, and learn about multi-cloud and hybrid AI strategies for enterprise adoption.
Data Management in the Cloud
Learn to design and implement scalable cloud storage solutions for AI data using services like Amazon S3, Azure Blob Storage, and Google Cloud Storage. This module covers architecting and populating data lakes and data warehouses for machine learning, utilizing cloud-native ETL and data preparation services (e.g., AWS Glue, Azure Data Factory, Google Cloud Dataproc). Students will build robust streaming data solutions with Kafka on MSK/Confluent, Azure Event Hubs, or Google Cloud Pub/Sub, and explore best practices for data governance, cataloging (e.g., AWS Glue Data Catalog, Azure Purview), and cost-optimizing cloud data storage.
Cloud-Based Model Development
Leverage managed notebook environments (e.g. SageMaker Studio, Azure ML Notebooks, Vertex AI Workbench) and integrated development environments (IDEs) for AI development. Implement containerized development workflows with Docker and Kubernetes. This module covers techniques for comprehensive experiment tracking and model versioning in the cloud using MLflow or cloud-native solutions. Students will utilize distributed training services, optimize GPU/TPU resource allocation, explore Automated Machine Learning (AutoML) platforms for rapid model prototyping, and apply version control (Git) for cloud AI development.
Model Deployment and Serving
Implement serverless deployment options for AI models using AWS Lambda, Azure Functions, or Google Cloud Functions. Orchestrate containers for scalable inference with Amazon EKS, Azure AKS, or Google Kubernetes Engine (GKE). Integrate AI services with API Gateways for secure and managed access, and implement dynamic auto-scaling strategies for model serving based on load. This module covers deploying lightweight models to edge devices via cloud services (e.g., AWS IoT Greengrass, Azure IoT Edge), and establishing robust model monitoring and management systems (e.g., Prometheus, Grafana) and building CI/CD pipelines for automated cloud AI deployment.
Managed AI Services
Gain deep insight into utilizing pre-built AI capabilities for Computer Vision (e.g., AWS Rekognition, Azure Computer Vision, Google Cloud Vision AI), Natural Language Processing (e.g., AWS Comprehend, Azure Text Analytics, Google Cloud Natural Language API), and Speech Recognition (e.g., AWS Transcribe, Azure Speech-to-Text, Google Cloud Speech-to-Text). This module explores custom model hosting services for specialized use cases, delves into AI marketplaces, and covers integrating third-party solutions. Students will understand cost models and optimization for managed services, and strategies for composing complex AI solutions using multiple cloud AI services.
Security and Compliance
Implement granular Identity and Access Management (IAM) for AI resources across cloud providers. Learn strategies for data encryption at rest and in transit, including KMS, Azure Key Vault, and Google Cloud KMS. Master network security best practices for AI applications, focusing on VPCs, subnets, security groups, and network access control lists. This module covers navigating and achieving compliance with industry frameworks (e.g., HIPAA, GDPR, SOC 2) in cloud AI environments. Techniques for privacy-preserving AI (e.g., differential privacy, federated learning) and establishing robust audit, governance, and responsible AI implementation practices in the cloud are also covered.

Students will design and implement a comprehensive, production-ready cloud-based AI solution. This project will demonstrate the efficient use of services across the entire AI lifecycle, from ingesting raw data into a cloud data lakeand performing distributed model training, to deploying a scalable, real-time inference endpoint. The project must incorporate key considerations for cost optimization, performance monitoring using cloud-native tools, robust security features, and adherence to specified compliance standards. A detailed technical report documenting the architecture, implementation decisions, performance benchmarks, and operational procedures is a required deliverable

AI in the Cloud

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AI in the Cloud

AI in the Cloud

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