Description
Introduction of Cloud Computing in AI& Automation
The rapid advancement in cloud computing has seen a significant shift towards integrating AI and automation to improve scalability, security, and efficiency. This training explores the evolving landscape of cloud technologies, focusing on how AI-driven automation can enhance cloud-based operations, optimize resources, and facilitate smarter decision-making. Participants will gain insights into how modern cloud infrastructure utilizes AI for tasks such as self-healing, auto-scaling, and predictive analysis, ultimately leading to more agile and cost-efficient cloud ecosystems.
Prerequisites
To gain the most from this training, participants should have:
- Basic understanding of cloud computing (e.g., knowledge of services like AWS, Google Cloud, or Microsoft Azure)
- Familiarity with cloud service models (IaaS, PaaS, SaaS)
- Basic knowledge of AI and machine learning concepts
- Experience in managing or deploying cloud solutions (optional but beneficial)
- Understanding of networking, storage, and virtualization (recommended)
Table of contents
- Foundations of Next-Gen Cloud Computing
1.1 Introduction to Next-Gen Cloud Technologies
1.2 Evolution of cloud computing
1.3 Role of AI and automation in modern cloud ecosystems
1.4 Core Concepts in AI-Driven Cloud Automation(Ref: Cloud-Based Marketing Automation in Salesforce (SFMC))
1.4.1 AI’s impact on cloud service delivery
1.4.2 Key automation technologies transforming cloud operations
1.5 Case Studies of Cloud Automation and AI Implementations - AI-Powered Infrastructure Management
2.1 AI in Cloud Infrastructure: An Overview
2.1.1 Self-healing systems
2.1.2 Auto-scaling and dynamic resource allocation
2.2 Automating Cloud Security with AI
2.2.1 AI-driven threat detection and response
2.2.2 Security automation for compliance and governance
2.3 Hands-On Lab: Setting up automated resource scaling and security configurations - Intelligent Automation in Cloud Operations
3.1 Automating Cloud Workflows and Operations
3.1.1 Task scheduling, workload balancing, and orchestration
3.1.2 AI for predictive maintenance and monitoring
3.2 AI and Automation for Cost Optimization
3.2.1 Smart resource management and billing predictions
3.2.2 Optimizing cloud costs using AI tools
3.3 Hands-On Lab: Automating cloud tasks using AI-based orchestration tools - Advanced AI Use Cases in Cloud Computing
4.1 AI and Machine Learning Models in the Cloud
4.1.1 Deploying and managing AI models at scale
4.1.2 Cloud services for ML (e.g., AWS SageMaker, Google AI Platform)
4.2 Edge Computing and AI: Synergies with the Cloud
4.2.1 Processing AI at the edge and cloud integration
4.3 Hands-On Lab: Implementing AI models in cloud environments - Future of Cloud Computing with AI and Automation
5.1 Trends in AI-Driven Cloud Computing
5.1.1 Emerging technologies: Serverless computing, Kubernetes, etc.
5.1.2 Autonomous cloud services
5.2 Building a Cloud Strategy for AI and Automation
5.2.1 Best practices for adopting AI-driven automation in the cloud
5.2.2 Future trends and the role of AI in cloud evolution
5.3 Final Project: Designing an AI and automation strategy for a cloud-based solution
This training is structured to balance theoretical concepts with practical hands-on experience, ensuring participants are well-equipped to apply AI and automation techniques in cloud environments.
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