AI on Cloud: Integrating Intelligence with Infrastructure
Transforming Cloud Infrastructure with Artificial Intelligence
Duration
5 Days (8 hours per day)
Level
Basic Level
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The “AI on Cloud: Integrating Intelligence with Infrastructure” program is designed to provide developers and IT professionals with the skills necessary to successfully integrate artificial intelligence within cloud computing environments. This course offers an in-depth exploration of advanced cloud infrastructure, AI model deployment, and the application of AI in cloud-based settings.
Through a hands-on, interactive approach, participants will engage with real-world AI projects, gaining practical experience in designing, deploying, and optimizing AI solutions on leading cloud platforms such as AWS, Azure, and GCP. By the end of the program, participants will be equipped to implement AI-driven strategies in cloud environments, ensuring sustained innovation and efficiency.
Outcomes:
Upon completion of the AI on Cloud: Integrating Intelligence with Infrastructure Program, participants will be able to:
- Develop and deploy AI models within various cloud environments
- Optimize AI models for performance, scalability, and cost-efficiency
- Integrate AI solutions with existing cloud-based systems
- Utilize cloud-based AI tools and platforms effectively
- Confidently tackle real-world AI+ Cloud integration challenges
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- Developers and IT professionals aiming to enhance their expertise in AI and cloud computing
- Individuals interested in cloud-based AI applications and solutions
- Professionals seeking to integrate AI into cloud environments effectively
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- Overview of AI and its applications in cloud computing
- Introduction to key cloud platforms: AWS, Azure, GCP
- Benefits and challenges of integrating AI with cloud environments
- Key trends and future directions in AI+Cloud integration
- Deep dive into cloud infrastructure components
- Overview of cloud-based AI services and tools
- Comparing AI offerings across AWS, Azure, and GCP
- Security and compliance considerations for AI on cloud
- Introduction to AWS services for AI model deployment
- Overview of Amazon SageMaker, AI/ML tools, and related services
- Hands-on project: Building and deploying a basic ML model on AWS
- Managing data pipelines and storage for AI models
- Advanced deployment strategies on AWS
- Performance tuning and cost optimization techniques on AWS
- Integrating AI models with AWS cloud-native applications
- Hands-on project: End-to-end AI model deployment and optimization on AWS
- Introduction to Azure AI services and machine learning tools
- Hands-on project: Building and deploying a basic ML model on Azure
- Overview of Azure Machine Learning Studio, Cognitive Services, and other AI tools
- Managing data pipelines and storage for AI models on Azure
- Advanced deployment strategies on Azure
- Performance tuning and cost optimization techniques on Azure
- Integrating AI models with Azure cloud-native applications
- Hands-on project: End-to-end AI model deployment and optimization on Azure
- Introduction to GCP’s AI tools and services
- Hands-on project: Building and deploying a basic ML model on GCP
- Overview of Google AI Platform, TensorFlow, and other AI tools
- Managing data pipelines and storage for AI models on GCP
- Advanced deployment strategies on GCP
- Performance tuning and cost optimization techniques on GCP
- Integrating AI models with GCP cloud-native applications
- Hands-on project: End-to-end AI model deployment and optimization on GCP
- Techniques for integrating AI solutions with existing cloud services
- Designing AI workflows and data management strategies
- Real-world use cases and integration strategies
- Hands-on project: Integration of AI models with cloud-based applications
- Strategies for scaling AI solutions in the cloud
- Techniques for improving model performance and efficiency
- Monitoring, logging, and troubleshooting AI models on cloud platforms
- Hands-on project: Optimizing an AI model for cloud deployment
- Understanding the lifecycle of AI projects in cloud environments
- Product management for AI solutions: Requirements, roadmap, and prioritization
- Collaboration between data scientists, developers, and product managers
- Agile methodologies and DevOps practices for AI+Cloud projects
- Ensuring ethical AI deployment in cloud environments
- Data privacy, security, and regulatory compliance
- AI governance frameworks and risk management
- Responsible AI practices and explainability
- Common challenges and solutions in AI & Cloud integration
- Case studies and real-world scenarios
- Industry-specific AI+Cloud integration examples
- Final project: Implementing an end-to-end AI solution in a cloud environment
- Hands-on final project: Design, deploy, and optimize a comprehensive AI solution in the cloud
- Peer reviews and feedback sessions
- Final project presentation and assessment
- Hands-on projects and case studies
- Quizzes and practical exercises
- Final project presentation and peer evaluation
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- Basic understanding of AI and cloud computing concepts
- Fundamental knowledge of computer science principles, including programming, data structures, and algorithms
- Familiarity with cloud platforms such as AWS, Azure, or GCP
- Basic mathematics knowledge relevant to machine learning
