Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-training-portal-advanced-video-course Lesson 1: Advanced IBM Cloud Architecture
1.1. Hybrid Cloud Integration
1.2. Multi-Cloud Management
1.3. Cloud Security Best Practices
1.4. Advanced Networking in IBM Cloud
1.5. Containerization with Kubernetes
1.6. Serverless Computing with IBM Cloud Functions
1.7. IBM Cloud Private Overview
1.8. Cloud Migration Strategies
1.9. Disaster Recovery Planning
1.10. Cost Optimization Techniques
Lesson 2: Deep Dive into IBM Watson AI
2.1. Natural Language Processing (NLP)
2.2. Machine Learning Models
2.3. Computer Vision Applications
2.4. Conversational AI with Watson Assistant
2.5. Data Preprocessing Techniques
2.6. Model Training and Evaluation
2.7. Deploying AI Models at Scale
2.8. Ethical Considerations in AI
2.9. Integrating Watson with Enterprise Systems
2.10. Case Studies in AI Implementation
Lesson 3: IBM Blockchain for Enterprise
3.1. Blockchain Fundamentals
3.2. Hyperledger Fabric Overview
3.3. Smart Contracts Development
3.4. Blockchain Network Configuration
3.5. Security and Privacy in Blockchain
3.6. Integrating Blockchain with IoT
3.7. Supply Chain Use Cases
3.8. Financial Services Applications
3.9. Governance and Compliance
3.10. Performance Tuning and Scalability
Lesson 4: Advanced Data Science with IBM
4.1. Data Wrangling and Cleaning
4.2. Feature Engineering Techniques
4.3. Advanced Statistical Analysis
4.4. Time Series Forecasting
4.5. Anomaly Detection Methods
4.6. Deep Learning Frameworks
4.7. Model Interpretability and Explainability
4.8. Big Data Analytics with Spark
4.9. Data Visualization Best Practices
4.10. Deploying Data Science Solutions
Lesson 5: IBM DevOps and CI/CD Pipelines
5.1. Continuous Integration Principles
5.2. Continuous Delivery Pipelines
5.3. Infrastructure as Code (IaC)
5.4. Automated Testing Strategies
5.5. Container Orchestration with Kubernetes
5.6. Monitoring and Logging
5.7. Security in DevOps
5.8. Blue-Green Deployments
5.9. Canary Releases
5.10. DevOps Culture and Best Practices
Lesson 6: IBM Quantum Computing
6.1. Quantum Computing Basics
6.2. Qiskit Framework Overview
6.3. Quantum Algorithms
6.4. Quantum Circuit Design
6.5. Quantum Error Correction
6.6. Quantum Machine Learning
6.7. Quantum Cryptography
6.8. Quantum Hardware and Architecture
6.9. Quantum Computing Use Cases
6.10. Future Trends in Quantum Computing
Lesson 7: IBM Security and Compliance
7.1. Identity and Access Management (IAM)
7.2. Threat Intelligence and Analytics
7.3. Incident Response Planning
7.4. Data Encryption Techniques
7.5. Compliance Frameworks (GDPR, HIPAA)
7.6. Security Information and Event Management (SIEM)
7.7. Penetration Testing and Vulnerability Assessment
7.8. Secure Software Development Lifecycle (SDLC)
7.9. Cloud Security Posture Management
7.10. Zero Trust Architecture
Lesson 8: IBM IoT Solutions
8.1. IoT Architecture and Protocols
8.2. Edge Computing Fundamentals
8.3. Device Management and Security
8.4. Data Ingestion and Processing
8.5. Real-Time Analytics
8.6. Predictive Maintenance
8.7. Smart Cities and Infrastructure
8.8. Industrial IoT Applications
8.9. IoT Integration with AI and ML
8.10. Scalability and Performance Optimization
Lesson 9: IBM Analytics and Business Intelligence
9.1. Data Warehousing Solutions
9.2. ETL Processes and Best Practices
9.3. Advanced SQL Querying
9.4. Data Governance and Quality
9.5. Dashboard Design and Visualization
9.6. Predictive Analytics Techniques
9.7. Integrating IBM Cognos Analytics
9.8. Real-Time Data Streaming
9.9. Customer Segmentation and Analysis
9.10. Financial Forecasting and Reporting
Lesson 10: IBM Mainframe and Z Systems
10.1. Mainframe Architecture Overview
10.2. z/OS Operating System
10.3. COBOL and Assembler Programming
10.4. Database Management with Db2
10.5. Mainframe Security and Compliance
10.6. Modernizing Legacy Systems
10.7. Integrating Mainframe with Cloud
10.8. Performance Tuning and Optimization
10.9. Disaster Recovery and High Availability
10.10. Future of Mainframe Technology
Lesson 11: IBM API Connect and Integration
11.1. API Design and Documentation
11.2. API Gateway Configuration
11.3. Security and Authentication
11.4. Rate Limiting and Throttling
11.5. API Monetization Strategies
11.6. Integrating with Microservices
11.7. Event-Driven Architecture
11.8. API Testing and Validation
11.9. Monitoring and Analytics
11.10. Best Practices for API Management
Lesson 12: IBM Garage Methodology
12.1. Agile and Lean Principles
12.2. Design Thinking Workshops
12.3. Rapid Prototyping Techniques
12.4. User-Centered Design
12.5. Minimum Viable Product (MVP) Development
12.6. Continuous Feedback Loops
12.7. Cross-Functional Team Collaboration
12.8. Innovation and Experimentation
12.9. Scaling Agile Practices
12.10. Case Studies in IBM Garage Implementation
Lesson 13: IBM Power Systems and AIX
13.1. Power Systems Architecture
13.2. AIX Operating System Fundamentals
13.3. System Administration and Management
13.4. Virtualization Techniques
13.5. High Availability and Clustering
13.6. Performance Monitoring and Tuning
13.7. Security and Compliance
13.8. Backup and Recovery Strategies
13.9. Integrating Power Systems with Cloud
13.10. Future Trends in Power Systems
Lesson 14: IBM Maximo Asset Management
14.1. Asset Lifecycle Management
14.2. Work Order Management
14.3. Inventory and Procurement
14.4. Predictive Maintenance Techniques
14.5. IoT Integration with Maximo
14.6. Reporting and Analytics
14.7. Mobile Solutions for Field Technicians
14.8. Compliance and Regulatory Management
14.9. Integrating Maximo with ERP Systems
14.10. Best Practices for Asset Management
Lesson 15: IBM Sterling Supply Chain Solutions
15.1. Order Management Systems
15.2. Inventory Optimization Techniques
15.3. Warehouse Management Solutions
15.4. Transportation and Logistics
15.5. Supplier Collaboration and Integration
15.6. Demand Forecasting and Planning
15.7. Real-Time Visibility and Tracking
15.8. Blockchain in Supply Chain
15.9. Sustainability and Green Supply Chain
15.10. Future Trends in Supply Chain Management
Lesson 16: IBM DataStage and ETL Processes
16.1. DataStage Architecture and Components
16.2. Designing ETL Workflows
16.3. Data Transformation Techniques
16.4. Performance Tuning and Optimization
16.5. Error Handling and Debugging
16.6. Integrating with Data Warehouses
16.7. Real-Time Data Integration
16.8. Data Quality and Governance
16.9. Automating ETL Processes
16.10. Best Practices for ETL Design
Lesson 17: IBM Websphere Application Server
17.1. Websphere Architecture and Components
17.2. Deploying Enterprise Applications
17.3. Clustering and High Availability
17.4. Security and Authentication
17.5. Performance Monitoring and Tuning
17.6. Integrating with Microservices
17.7. DevOps Practices with Websphere
17.8. Migration Strategies
17.9. Troubleshooting and Debugging
17.10. Future Trends in Application Servers
Lesson 18: IBM Guardium Data Protection
18.1. Data Discovery and Classification
18.2. Database Activity Monitoring
18.3. Encryption and Tokenization
18.4. Compliance and Audit Reporting
18.5. Threat Detection and Response
18.6. Integrating with SIEM Systems
18.7. Data Masking Techniques
18.8. Cloud Data Protection Strategies
18.9. Best Practices for Data Security
18.10. Future Trends in Data Protection
Lesson 19: IBM MQ and Messaging Solutions
19.1. MQ Architecture and Components
19.2. Message Queuing Fundamentals
19.3. Designing Messaging Solutions
19.4. Security and Authentication
19.5. High Availability and Clustering
19.6. Integrating with Microservices
19.7. Performance Tuning and Optimization
19.8. Monitoring and Analytics
19.9. Troubleshooting and Debugging
19.10. Best Practices for Messaging Solutions
Lesson 20: IBM Cloud Pak for Data
20.1. Cloud Pak Architecture and Components
20.2. Data Virtualization Techniques
20.3. Integrating with Data Lakes
20.4. AI and Machine Learning Workflows
20.5. Data Governance and Quality
20.6. Real-Time Data Streaming
20.7. Multi-Cloud Data Management
20.8. Security and Compliance
20.9. Performance Tuning and Optimization
20.10. Future Trends in Data Management
Lesson 21: IBM Cloud Pak for Automation
21.1. Automation Architecture and Components
21.2. Robotic Process Automation (RPA)
21.3. Workflow Orchestration
21.4. Decision Management Solutions
21.5. Integrating with Enterprise Systems
21.6. AI-Driven Automation
21.7. Compliance and Governance
21.8. Performance Monitoring and Tuning
21.9. Best Practices for Automation
21.10. Future Trends in Automation
Lesson 22: IBM Cloud Pak for Integration
22.1. Integration Architecture and Components
22.2. API Management and Gateway
22.3. Event Streams and Messaging
22.4. Data Transformation and Mapping
22.5. Integrating with Cloud Services
22.6. Security and Compliance
22.7. Performance Tuning and Optimization
22.8. Monitoring and Analytics
22.9. Best Practices for Integration
22.10. Future Trends in Integration
Lesson 23: IBM Cloud Pak for Multicloud Management
23.1. Multicloud Management Architecture
23.2. Hybrid Cloud Integration
23.3. Container Orchestration with Kubernetes
23.4. Infrastructure as Code (IaC)
23.5. Cost Management and Optimization
23.6. Security and Compliance
23.7. Performance Monitoring and Tuning
23.8. Automated Provisioning and Scaling
23.9. Best Practices for Multicloud Management
23.10. Future Trends in Multicloud Management
Lesson 24: IBM Cloud Pak for Security
24.1. Security Architecture and Components
24.2. Threat Intelligence and Analytics
24.3. Identity and Access Management (IAM)
24.4. Data Protection and Encryption
24.5. Incident Response and Management
24.6. Compliance and Governance
24.7. Integrating with SIEM Systems
24.8. Performance Tuning and Optimization
24.9. Best Practices for Security Management
24.10. Future Trends in Security
Lesson 25: IBM Cloud Pak for Watson AIOps
25.1. AIOps Architecture and Components
25.2. Anomaly Detection and Prediction
25.3. Incident Management and Resolution
25.4. Integrating with IT Operations
25.5. Performance Monitoring and Tuning
25.6. Automated Remediation Workflows
25.7. Compliance and Governance
25.8. Best Practices for AIOps
25.9. Future Trends in AIOps
25.10. Case Studies in AIOps Implementation
Lesson 26: IBM Cloud Pak for Business Automation
26.1. Business Automation Architecture
26.2. Workflow and Case Management
26.3. Content Management Solutions
26.4. Decision Management and Rules
26.5. Integrating with Enterprise Systems
26.6. AI-Driven Automation
26.7. Compliance and Governance
26.8. Performance Monitoring and Tuning
26.9. Best Practices for Business Automation
26.10. Future Trends in Business Automation
Lesson 27: IBM Cloud Pak for DataOps
27.1. DataOps Architecture and Components
27.2. Data Pipeline Orchestration
27.3. Data Quality and Governance
27.4. Real-Time Data Integration
27.5. AI and Machine Learning Workflows
27.6. Security and Compliance
27.7. Performance Tuning and Optimization
27.8. Best Practices for DataOps
27.9. Future Trends in DataOps
27.10. Case Studies in DataOps Implementation
Lesson 28: IBM Cloud Pak for Edge
28.1. Edge Computing Architecture
28.2. Deploying Edge Applications
28.3. Data Management at the Edge
28.4. Security and Compliance
28.5. Integrating with Cloud Services
28.6. Performance Monitoring and Tuning
28.7. Use Cases in Edge Computing
28.8. Best Practices for Edge Deployment
28.9. Future Trends in Edge Computing
28.10. Case Studies in Edge Computing
Lesson 29: IBM Cloud Pak for Network Automation
29.1. Network Automation Architecture
29.2. Intent-Based Networking
29.3. Automating Network Operations
29.4. Integrating with SDN and NFV
29.5. Security and Compliance
29.6. Performance Monitoring and Tuning
29.7. Best Practices for Network Automation
29.8. Future Trends in Network Automation
29.9. Case Studies in Network Automation
29.10. Advanced Network Analytics
Lesson 30: IBM Cloud Pak for Aspera
30.1. Aspera Architecture and Components
30.2. High-Speed Data Transfer
30.3. Secure File Sharing Solutions
30.4. Integrating with Cloud Storage
30.5. Automating Data Workflows
30.6. Performance Monitoring and Tuning
30.7. Compliance and Governance
30.8. Best Practices for Data Transfer
30.9. Future Trends in Data Transfer
30.10. Case Studies in Aspera Implementation
Lesson 31: IBM Cloud Pak for Data Replication
31.1. Data Replication Architecture
31.2. Real-Time Data Synchronization
31.3. Disaster Recovery Solutions
31.4. Integrating with Databases
31.5. Security and Compliance
31.6. Performance Monitoring and Tuning
31.7. Best Practices for Data Replication
31.8. Future Trends in Data Replication
31.9. Case Studies in Data Replication
31.10. Advanced Replication Techniques
Lesson 32: IBM Cloud Pak for FinOps
32.1. FinOps Architecture and Components
32.2. Cost Management and Optimization
32.3. Budgeting and Forecasting
32.4. Integrating with Financial Systems
32.5. Compliance and Governance
32.6. Performance Monitoring and Tuning
32.7. Best Practices for FinOps
32.8. Future Trends in FinOps
32.9. Case Studies in FinOps Implementation
32.10. Advanced Financial Analytics
Lesson 33: IBM Cloud Pak for DevSecOps
33.1. DevSecOps Architecture and Components
33.2. Integrating Security into DevOps
33.3. Automated Security Testing
33.4. Compliance and Governance
33.5. Incident Response and Management
33.6. Performance Monitoring and Tuning
33.7. Best Practices for DevSecOps
33.8. Future Trends in DevSecOps
33.9. Case Studies in DevSecOps Implementation
33.10. Advanced Security Analytics
Lesson 34: IBM Cloud Pak for Telco Network Cloud Manager
34.1. Telco Network Architecture
34.2. Automating Network Operations
34.3. Integrating with 5G Networks
34.4. Security and Compliance
34.5. Performance Monitoring and Tuning
34.6. Best Practices for Telco Network Management
34.7. Future Trends in Telco Networks
34.8. Case Studies in Telco Network Management
34.9. Advanced Network Analytics
34.10. Edge Computing in Telco Networks
Lesson 35: IBM Cloud Pak for Data Governance
35.1. Data Governance Architecture
35.2. Data Quality and Lineage
35.3. Metadata Management
35.4. Compliance and Regulatory Reporting
35.5. Integrating with Data Lakes
35.6. Security and Access Control
35.7. Performance Monitoring and Tuning
35.8. Best Practices for Data Governance
35.9. Future Trends in Data Governance
35.10. Case Studies in Data Governance
Lesson 36: IBM Cloud Pak for Edge Analytics
36.1. Edge Analytics Architecture
36.2. Real-Time Data Processing
36.3. AI and Machine Learning at the Edge
36.4. Integrating with IoT Devices
36.5. Security and Compliance
36.6. Performance Monitoring and Tuning
36.7. Use Cases in Edge Analytics
36.8. Best Practices for Edge Analytics
36.9. Future Trends in Edge Analytics
36.10. Case Studies in Edge Analytics
Lesson 37: IBM Cloud Pak for Data Virtualization
37.1. Data Virtualization Architecture
37.2. Integrating Disparate Data Sources
37.3. Real-Time Data Access
37.4. Security and Access Control
37.5. Performance Monitoring and Tuning
37.6. Best Practices for Data Virtualization
37.7. Future Trends in Data Virtualization
37.8. Case Studies in Data Virtualization
37.9. Advanced Data Integration Techniques
37.10. Data Governance in Virtualization
Lesson 38: IBM Cloud Pak for AI Ops
38.1. AI Ops Architecture and Components
38.2. Anomaly Detection and Prediction
38.3. Incident Management and Resolution
38.4. Integrating with IT Operations
38.5. Performance Monitoring and Tuning
38.6. Automated Remediation Workflows
38.7. Compliance and Governance
38.8. Best Practices for AI Ops
38.9. Future Trends in AI Ops
38.10. Case Studies in AI Ops Implementation
Lesson 39: IBM Cloud Pak for Data Fabric
39.1. Data Fabric Architecture and Components
39.2. Data Integration and Management
39.3. Metadata and Data Lineage
39.4. Security and Access Control
39.5. Performance Monitoring and Tuning
39.6. Best Practices for Data Fabric
39.7. Future Trends in Data Fabric
39.8. Case Studies in Data Fabric Implementation
39.9. Advanced Data Governance Techniques
39.10. Real-Time Data Access and Analytics
Lesson 40: IBM Cloud Pak for Edge Computing
40.1. Edge Computing Architecture
40.2. Deploying Edge Applications
40.3. Data Management at the Edge
40.4. Security and Compliance
40.5. Integrating with Cloud Services
40.6. Performance Monitoring and Tuning
40.7. Use Cases in Edge Computing
40.8. Best Practices for Edge Deployment
40.9. Future Trends in Edge Computing