Scalable AI Solutions in the Cloud
( 30 Modules )

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

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Scalable AI Solutions in the Cloud ( 30 Modules )

Scalable AI Solutions in the Cloud
( 30 Modules )