Design and Implementation of Cloud-Based AI Systems
( 30 Modules )

Module #1
Introduction to Cloud-Based AI
Overview of AI and cloud computing, benefits and challenges of cloud-based AI systems

Module #2
Cloud Computing Fundamentals
Overview of cloud computing models (IaaS, PaaS, SaaS), deployment models (public, private, hybrid), and cloud providers (AWS, Azure, GCP)

Module #3
AI and Machine Learning Basics
Introduction to AI, machine learning, and deep learning, key concepts and algorithms

Module #4
Cloud-Based AI Services
Overview of cloud-based AI services (Azure Machine Learning, SageMaker, Google Cloud AI Platform), features and pricing

Module #5
Design Principles for Cloud-Based AI Systems
Best practices for designing scalable, secure, and efficient cloud-based AI systems

Module #6
Cloud-Based Data Storage for AI
Overview of cloud-based data storage options (object storage, file storage, database storage), data lakes, and data warehousing

Module #7
Data Ingestion and Processing for AI
Overview of data ingestion and processing techniques for cloud-based AI systems, including data pipelines and ETL

Module #8
Cloud-Based AI Frameworks and Tools
Overview of popular cloud-based AI frameworks and tools (TensorFlow, PyTorch, Keras), features and use cases

Module #9
Building Cloud-Based AI Models
Hands-on lab:Building and deploying a simple AI model using a cloud-based AI service

Module #10
Model Training and Tuning for Cloud-Based AI
Best practices for training and tuning AI models for cloud-based AI systems, including hyperparameter tuning and model optimization

Module #11
Model Deployment and Management for Cloud-Based AI
Overview of model deployment and management strategies for cloud-based AI systems, including model serving and monitoring

Module #12
Cloud-Based AI Security and Governance
Best practices for securing and governing cloud-based AI systems, including data encryption, access control, and compliance

Module #13
Cloud-Based AI Cost Optimization
Strategies for optimizing costs for cloud-based AI systems, including instance selection, pricing models, and cost estimation

Module #14
Scaling Cloud-Based AI Systems
Best practices for scaling cloud-based AI systems, including horizontal scaling, vertical scaling, and load balancing

Module #15
Cloud-Based AI Monitoring and Logging
Overview of monitoring and logging tools and strategies for cloud-based AI systems, including CloudWatch, CloudTrail, and Loggly

Module #16
Cloud-Based AI Case Studies
Real-world case studies of cloud-based AI systems, including industries and applications

Module #17
Cloud-Based AI Project Planning and Management
Best practices for planning and managing cloud-based AI projects, including agile methodologies and project planning tools

Module #18
Cloud-Based AI Ethics and Explainability
Overview of ethical considerations for cloud-based AI systems, including bias, fairness, and explainability

Module #19
Cloud-Based AI for Computer Vision
Overview of cloud-based AI services and tools for computer vision, including image classification, object detection, and image segmentation

Module #20
Cloud-Based AI for Natural Language Processing
Overview of cloud-based AI services and tools for natural language processing, including text classification, sentiment analysis, and language translation

Module #21
Cloud-Based AI for Predictive Analytics
Overview of cloud-based AI services and tools for predictive analytics, including regression, classification, and clustering

Module #22
Cloud-Based AI for Recommendation Systems
Overview of cloud-based AI services and tools for recommendation systems, including collaborative filtering and content-based filtering

Module #23
Cloud-Based AI for Time Series Analysis
Overview of cloud-based AI services and tools for time series analysis, including forecasting, anomaly detection, and clustering

Module #24
Advanced Cloud-Based AI Topics
Overview of advanced cloud-based AI topics, including edge AI, IoT, and transfer learning

Module #25
Cloud-Based AI Project Development
Hands-on lab:Developing a cloud-based AI project, including data preparation, model training, and deployment

Module #26
Cloud-Based AI Project Deployment and Maintenance
Hands-on lab:Deploying and maintaining a cloud-based AI project, including model serving, monitoring, and optimization

Module #27
Cloud-Based AI in Industry Verticals
Overview of cloud-based AI applications in industry verticals, including healthcare, finance, and retail

Module #28
Cloud-Based AI Trends and Future Directions
Overview of emerging trends and future directions in cloud-based AI, including quantum AI and edge AI

Module #29
Cloud-Based AI Project Presentations
Student project presentations, including cloud-based AI project demonstrations and feedback

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Design and Implementation of Cloud-Based AI Systems career

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Design and Implementation of Cloud-Based AI Systems ( 30 Modules )

Design and Implementation of Cloud-Based AI Systems
( 30 Modules )