Module #1 Introduction to Scalable AI Solutions in the Cloud Overview of the importance of scalability in AI solutions and the benefits of deploying them in the cloud
Module #2 Cloud Computing Fundamentals for AI Introduction to cloud computing concepts, including IaaS, PaaS, and SaaS, and their relevance to AI workloads
Module #3 AI and Machine Learning in the Cloud Overview of popular AI and ML frameworks and their cloud-based implementations
Module #4 Designing Scalable AI Architectures Principles and best practices for designing scalable AI architectures, including microservices and containerization
Module #5 Cloud-Native AI Services Overview of cloud-native AI services, including AWS SageMaker, Google Cloud AI Platform, and Azure Machine Learning
Module #6 Scalable Data Storage for AI Options for scalable data storage for AI workloads, including object storage, file systems, and databases
Module #7 Data Ingestion and Processing for AI Techniques for ingesting and processing large datasets for AI workloads, including data pipelines and ETL tools
Module #8 Distributed Computing for AI Overview of distributed computing concepts, including parallel processing and clustering, for AI workloads
Module #9 Containerization for AI Workloads Using containerization (e.g. Docker) to deploy and manage AI workloads in the cloud
Module #10 Serverless AI with Cloud Functions Using serverless computing (e.g. AWS Lambda, Google Cloud Functions) for AI workloads
Module #11 Orchestrating AI Workflows with Kubernetes Using Kubernetes to orchestrate and manage AI workflows in the cloud
Module #12 AI Model Training at Scale Techniques for training AI models at scale, including distributed training and hyperparameter tuning
Module #13 Model Deployment and Serving Deploying and serving AI models in the cloud, including model inference and explanation
Module #14 AI Model Operations (MLOps) Principles and practices for MLOps, including model monitoring, versioning, and lifecycle management
Module #15 Explainability and Transparency in AI Techniques for explaining and interpreting AI models, including model interpretability and visualizations
Module #16 AI Security and Compliance in the Cloud Best practices for securing AI workloads in the cloud, including data encryption, access control, and compliance
Module #17 Cost Optimization for AI Workloads Strategies for optimizing costs for AI workloads in the cloud, including resource utilization and pricing models
Module #18 Monitoring and Troubleshooting AI Workloads Techniques for monitoring and troubleshooting AI workloads in the cloud, including logging, metrics, and debugging
Module #19 Case Studies in Scalable AI Solutions Real-world examples of scalable AI solutions in the cloud, including use cases and lessons learned
Module #20 Best Practices for AI Adoption in the Cloud Guidelines for adopting AI solutions in the cloud, including change management, skills, and organizational readiness
Module #21 Future of Scalable AI Solutions in the Cloud Emerging trends and future directions in scalable AI solutions in the cloud, including edge AI and quantum AI
Module #22 Hands-on Lab 1:Deploying AI Models on Cloud-Native Services Hands-on lab exercise deploying AI models on cloud-native services such as AWS SageMaker or Google Cloud AI Platform
Module #23 Hands-on Lab 2:Building Scalable AI Pipelines with Kubernetes Hands-on lab exercise building scalable AI pipelines using Kubernetes
Module #24 Hands-on Lab 3:Optimizing AI Workloads for Cost and Performance Hands-on lab exercise optimizing AI workloads for cost and performance in the cloud
Module #25 Hands-on Lab 4:Securing AI Workloads in the Cloud Hands-on lab exercise securing AI workloads in the cloud, including data encryption and access control
Module #26 Hands-on Lab 5:Explaining and Visualizing AI Models Hands-on lab exercise explaining and visualizing AI models using techniques such as model interpretability and visualizations
Module #27 Hands-on Lab 6:Monitoring and Troubleshooting AI Workloads Hands-on lab exercise monitoring and troubleshooting AI workloads in the cloud
Module #28 Capstone Project:Designing a Scalable AI Solution Capstone project requiring students to design a scalable AI solution in the cloud, including architecture, deployment, and optimization
Module #29 Peer Review and Feedback Peer review and feedback on capstone projects
Module #30 Course Wrap-Up & Conclusion Planning next steps in Scalable AI Solutions in the Cloud career
