Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-iot-centralized-monitoring-advanced-video-course Lesson 1: Introduction to Expert-Level IoT Monitoring Concepts
1.01 Review of Core IBM IoT Platform Architecture for Monitoring
1.02 Defining "Expert-Level" Monitoring Requirements
1.03 Advanced Use Cases for Centralized IoT Monitoring
1.04 Understanding the Evolution of IoT Monitoring Needs
1.05 Key Performance Indicators (KPIs) for Advanced Monitoring
1.06 Identifying Common Monitoring Challenges at Scale
1.07 The Role of Centralized Platforms in Complex IoT Deployments
1.08 Setting the Stage for Deep Dive into IBM IoT Centralized Monitoring
1.09 Course Objectives and Learning Outcomes
1.10 Prerequisites and Expected Skill Levels
Lesson 2: Advanced IBM IoT Platform Service Integration for Monitoring
2.01 Deep Dive into IBM Watson IoT Platform Connectivity Options
2.02 Integrating IoT Platform with IBM Cloud Services for Monitoring
2.03 Leveraging IBM Event Streams for Real-time Monitoring Data
2.04 Utilizing IBM Cloud Functions for Alerting and Automation
2.05 Connecting with IBM Cloud Object Storage for Historical Data
2.06 Integrating with IBM Cloud Databases for Monitoring Configuration
2.07 Understanding Service Bindings and Authentication Methods
2.08 Best Practices for Secure Service Integration
2.09 Troubleshooting Common Integration Issues
2.10 Demonstration: Setting up a Multi-Service Monitoring Pipeline
Lesson 3: Advanced Device Management and Data Ingestion Strategies
3.01 Scaling Device Registration and Management Techniques
3.02 Implementing Advanced Device Type Configurations
3.03 Handling High-Throughput Device Data Ingestion
3.04 Managing Device Lifecycle Events for Monitoring
3.05 Utilizing Custom Device Properties and Metadata for Filtering
3.06 Strategies for Dealing with Disconnected or Offline Devices
3.07 Implementing Quality of Service (QoS) Levels for Monitoring Data
3.08 Monitoring Device Health and Connectivity Status at Scale
3.09 Advanced Error Handling for Data Ingestion
3.10 Demonstration: Onboarding and Monitoring a Large Fleet of Devices
Lesson 4: Customizing and Extending the IBM IoT Platform for Monitoring
4.01 Exploring the Capabilities of the IBM IoT Platform API
4.02 Developing Custom Monitoring Applications using the API
4.03 Extending Device Type Functionality for Monitoring
4.04 Creating Custom Dashboards and Visualizations
4.05 Implementing Custom Rules and Actions for Monitoring Events
4.06 Leveraging the Node-RED Integration for Custom Logic
4.07 Building Custom Data Processing Pipelines
4.08 Packaging and Deploying Custom Monitoring Solutions
4.09 Best Practices for API Usage and Security
4.10 Demonstration: Building a Simple Custom Monitoring Application
Lesson 5: Advanced Rule Engine Configuration and Automation
5.01 Deep Dive into the IBM IoT Platform Rule Engine
5.02 Creating Complex Rules with Multiple Conditions
5.03 Utilizing Advanced Data Transformations within Rules
5.04 Implementing Time-Based and Aggregation Rules
5.05 Configuring Rule Actions for Notifications and Automation
5.06 Integrating Rules with External Systems via Webhooks
5.07 Managing and Monitoring Rule Execution Performance
5.08 Troubleshooting Rule Configuration Issues
5.09 Strategies for Managing a Large Number of Rules
5.10 Demonstration: Implementing a Complex Monitoring Rule with Automation
Lesson 6: Implementing Advanced Alerting and Notification Systems
6.01 Designing a Comprehensive Alerting Strategy
6.02 Configuring Different Types of Alerts (Threshold, Anomaly, etc.)
6.03 Utilizing Multiple Notification Channels (Email, SMS, Push Notifications)
6.04 Implementing Alert Escalation Policies
6.05 Integrating with Incident Management Systems (e.g., PagerDuty)
6.06 Customizing Alert Messages and Content
6.07 Monitoring and Managing Alert Volume
6.08 Best Practices for Reducing Alert Fatigue
6.09 Testing and Validating Alerting Configurations
6.10 Demonstration: Setting up a Multi-Channel Alerting System
Lesson 7: Advanced Data Visualization and Dashboarding Techniques
7.01 Designing Effective Monitoring Dashboards for Different Stakeholders
7.02 Utilizing Advanced Chart Types and Widgets
7.03 Integrating Data from Multiple Sources into Dashboards
7.04 Implementing Real-time Data Streaming in Dashboards
7.05 Creating Interactive Dashboards for Drill-down Analysis
7.06 Customizing Dashboard Layouts and Themes
7.07 Sharing and Collaborating on Dashboards
7.08 Performance Optimization for Dashboards with Large Datasets
7.09 Troubleshooting Dashboard Loading Issues
7.10 Demonstration: Building an Advanced Monitoring Dashboard
Lesson 8: Leveraging Historical Data and Analytics for Monitoring
8.01 Strategies for Storing and Managing Historical IoT Data
8.02 Integrating with Data Lakes and Data Warehouses
8.03 Utilizing IBM Cloud Databases for Historical Data Analysis
8.04 Applying Analytical Techniques to Identify Trends and Anomalies
8.05 Implementing Predictive Monitoring using Historical Data
8.06 Generating Reports and Insights from Historical Data
8.07 Data Retention and Archiving Strategies
8.08 Querying and Analyzing Large Volumes of Historical Data
8.09 Understanding Data Privacy and Compliance for Historical Data
8.10 Demonstration: Analyzing Historical IoT Data for Insights
Lesson 9: Implementing Advanced Security for IoT Monitoring
9.01 Securing Device Connectivity for Monitoring Data
9.02 Implementing Authentication and Authorization for Monitoring Users
9.03 Utilizing Access Control Lists (ACLs) for Data Access
9.04 Encrypting Data in Transit and At Rest
9.05 Monitoring for Security Breaches and Anomalies
9.06 Implementing Auditing and Logging for Monitoring Activities
9.07 Integrating with Security Information and Event Management (SIEM) Systems
9.08 Best Practices for Securing Custom Monitoring Applications
9.09 Responding to Security Incidents in a Monitoring Context
9.10 Demonstration: Implementing Security Measures for Monitoring Data
Lesson 10: Performance Monitoring and Optimization of the IBM IoT Platform
10.01 Identifying Key Performance Indicators (KPIs) for Platform Health
10.02 Monitoring Message Throughput and Latency
10.03 Analyzing Rule Engine Performance
10.04 Monitoring Database Performance for Monitoring Data
10.05 Identifying and Resolving Bottlenecks in the Monitoring Pipeline
10.06 Scaling the IoT Platform for Increased Monitoring Load
10.07 Utilizing IBM Cloud Monitoring and Logging Services
10.08 Implementing Proactive Performance Monitoring
10.09 Capacity Planning for Future Monitoring Needs
10.10 Demonstration: Monitoring and Optimizing Platform Performance
Lesson 11: Advanced Integration with Third-Party Monitoring Tools
11.01 Exploring Common Third-Party Monitoring Tools (e.g., Splunk, Grafana, Datadog)
11.02 Strategies for Integrating IBM IoT Data with External Platforms
11.03 Utilizing APIs and Webhooks for Data Export
11.04 Building Custom Connectors for Integration
11.05 Leveraging Data Transformation and Mapping for Compatibility
11.06 Implementing Two-Way Integration for Command and Control
11.07 Securing Data Exchange with Third-Party Tools
11.08 Troubleshooting Integration Issues with External Systems
11.09 Best Practices for Choosing and Implementing Third-Party Tools
11.10 Demonstration: Integrating IBM IoT Data with a Third-Party Dashboard
Lesson 12: Implementing Predictive Maintenance Monitoring
12.01 Understanding the Principles of Predictive Maintenance
12.02 Identifying Relevant Data for Predictive Monitoring
12.03 Utilizing Anomaly Detection Techniques
12.04 Integrating with Machine Learning Models (e.g., Watson Studio)
12.05 Implementing Model Deployment and Scoring for Predictions
12.06 Configuring Alerts Based on Predictive Insights
12.07 Monitoring Model Performance and Retraining Strategies
12.08 Integrating Predictive Insights into Monitoring Dashboards
12.09 Measuring the Effectiveness of Predictive Maintenance Monitoring
12.10 Demonstration: Setting up a Basic Predictive Maintenance Monitoring Scenario
Lesson 13: Monitoring Edge Computing Devices and Gateways
13.01 Challenges of Monitoring Devices at the Edge
13.02 Utilizing IBM Edge Application Manager for Monitoring Edge Devices
13.03 Collecting and Aggregating Data from Edge Gateways
13.04 Implementing Local Monitoring and Alerting at the Edge
13.05 Synchronizing Edge Monitoring Data with the Cloud Platform
13.06 Managing and Monitoring Edge Application Deployments
13.07 Handling Offline Scenarios for Edge Monitoring
13.08 Security Considerations for Edge Monitoring
13.09 Troubleshooting Edge Monitoring Issues
13.10 Demonstration: Monitoring Devices Connected via an Edge Gateway
Lesson 14: Implementing Geolocation-Based Monitoring
14.01 Utilizing Device Location Data for Monitoring
14.02 Implementing Geofencing for Location-Based Alerts
14.03 Visualizing Device Locations on Maps
14.04 Tracking Device Movement and Routes
14.05 Integrating with Mapping Services (e.g., Mapbox, Google Maps)
14.06 Handling GPS Data Accuracy and Reliability
14.07 Implementing Location-Based Rules and Actions
14.08 Security and Privacy Considerations for Location Data
14.09 Troubleshooting Geolocation Data Issues
14.10 Demonstration: Setting up Geofencing and Location Tracking
Lesson 15: Monitoring Environmental and Sensor Data
15.01 Collecting and Processing Various Sensor Data Types
15.02 Handling Different Sensor Data Formats
15.03 Implementing Data Calibration and Filtering
15.04 Monitoring Environmental Conditions (Temperature, Humidity, etc.)
15.05 Utilizing Thresholds and Ranges for Sensor Data
15.06 Visualizing Sensor Data Trends and Patterns
15.07 Integrating with Weather and Environmental Data Services
15.08 Troubleshooting Sensor Data Inaccuracies
15.09 Best Practices for Monitoring Sensor Networks
15.10 Demonstration: Monitoring and Visualizing Environmental Sensor Data
Lesson 16: Monitoring Industrial Equipment and Assets
16.01 Understanding Industrial Protocols and Data Formats
16.02 Integrating with SCADA and PLC Systems
16.03 Monitoring Equipment Status and Performance
16.04 Utilizing Asset Hierarchies for Monitoring
16.05 Implementing Condition Monitoring Techniques
16.06 Integrating with Enterprise Asset Management (EAM) Systems
16.07 Handling Data from Legacy Industrial Systems
16.08 Security Considerations for Industrial Monitoring
16.09 Troubleshooting Industrial Data Connectivity
16.10 Demonstration: Monitoring an Industrial Asset
Lesson 17: Monitoring Connected Vehicles and Telematics Data
17.01 Collecting and Processing Vehicle Data (Speed, Location, Engine Status)
17.02 Utilizing Vehicle Telematics Protocols
17.03 Implementing Fleet Monitoring and Management
17.04 Monitoring Driver Behavior and Safety
17.05 Integrating with Vehicle Diagnostic Systems
17.06 Handling Large Volumes of Telematics Data
17.07 Implementing Real-time Vehicle Tracking and Alerts
17.08 Security and Privacy Considerations for Vehicle Data
17.09 Troubleshooting Telematics Data Issues
17.10 Demonstration: Monitoring a Fleet of Connected Vehicles
Lesson 18: Monitoring Healthcare Devices and Patient Data
18.01 Understanding Healthcare Data Standards and Regulations (e.g., HIPAA)
18.02 Collecting and Processing Medical Device Data
18.03 Implementing Remote Patient Monitoring
18.04 Monitoring Vital Signs and Health Metrics
18.05 Implementing Alerting for Critical Health Events
18.06 Integrating with Electronic Health Record (EHR) Systems
18.07 Ensuring Data Privacy and Security for Patient Data
18.08 Compliance Considerations for Healthcare Monitoring
18.09 Troubleshooting Medical Device Connectivity
18.10 Demonstration: Monitoring a Healthcare Device
Lesson 19: Monitoring Smart City Infrastructure
19.01 Understanding Smart City Use Cases for Monitoring
19.02 Monitoring Public Utilities (Water, Electricity, Gas)
19.03 Monitoring Transportation Systems (Traffic, Public Transit)
19.04 Monitoring Environmental Factors (Air Quality, Noise Levels)
19.05 Integrating Data from Various City Systems
19.06 Implementing Public Safety Monitoring
19.07 Handling Data from Large-Scale Sensor Networks
19.08 Security and Privacy Considerations for Smart City Data
19.09 Troubleshooting Smart City Data Integration
19.10 Demonstration: Monitoring a Smart City Infrastructure Component
Lesson 20: Monitoring Retail and Supply Chain Operations
20.01 Understanding Retail and Supply Chain Monitoring Needs
20.02 Monitoring Inventory Levels and Movement
20.03 Tracking Goods in Transit
20.04 Monitoring Store Conditions (Temperature, Security)
20.05 Integrating with Point of Sale (POS) Systems
20.06 Implementing Cold Chain Monitoring
20.07 Utilizing Location Data for Supply Chain Visibility
20.08 Security Considerations for Retail and Supply Chain Data
20.09 Troubleshooting Supply Chain Data Issues
20.10 Demonstration: Monitoring a Retail Store or Supply Chain Segment
Lesson 21: Advanced User Management and Access Control
21.01 Implementing Role-Based Access Control (RBAC) for Monitoring Users
21.02 Configuring Fine-Grained Permissions for Monitoring Resources
21.03 Integrating with Enterprise Directory Services (e.g., LDAP, Active Directory)
21.04 Managing User Groups and Organizations
21.05 Auditing User Activity for Monitoring Compliance
21.06 Implementing Multi-Factor Authentication (MFA)
21.07 Best Practices for User Onboarding and Offboarding
21.08 Troubleshooting Access Control Issues
21.09 Security Considerations for User Management
21.10 Demonstration: Configuring Advanced User Permissions
Lesson 22: Monitoring for Compliance and Regulatory Requirements
22.01 Understanding Industry-Specific Compliance Standards (e.g., HIPAA, GDPR, ISO 27001)
22.02 Implementing Monitoring Strategies to Meet Compliance Needs
22.03 Utilizing Auditing and Logging for Compliance Reporting
22.04 Configuring Data Retention Policies for Compliance
22.05 Implementing Data Masking and Anonymization
22.06 Integrating with Compliance Management Platforms
22.07 Preparing for Compliance Audits
22.08 Troubleshooting Compliance-Related Monitoring Issues
22.09 Best Practices for Maintaining Compliance
22.10 Demonstration: Configuring Monitoring for a Specific Compliance Requirement
Lesson 23: Disaster Recovery and Business Continuity for Monitoring
23.01 Identifying Critical Monitoring Components
23.02 Implementing High Availability for Monitoring Services
23.03 Configuring Data Replication and Backup Strategies
23.04 Developing a Disaster Recovery Plan for Monitoring
23.05 Testing and Validating Disaster Recovery Procedures
23.06 Implementing Failover Mechanisms for Monitoring Components
23.07 Utilizing Multi-Region Deployments for Resilience
23.08 Monitoring the Health of Disaster Recovery Systems
23.09 Troubleshooting Disaster Recovery Failures
23.10 Demonstration: Simulating a Disaster Recovery Scenario
Lesson 24: Cost Management and Optimization for Monitoring
24.01 Understanding the Cost Drivers of IBM IoT Monitoring
24.02 Monitoring Resource Usage and Consumption
24.03 Optimizing Data Ingestion and Processing Costs
24.04 Managing Storage Costs for Historical Data
24.05 Identifying Opportunities for Cost Reduction
24.06 Utilizing Cost Monitoring Tools and Reports
24.07 Implementing Resource Quotas and Limits
24.08 Forecasting Future Monitoring Costs
24.09 Best Practices for Cost Optimization
24.10 Demonstration: Analyzing and Optimizing Monitoring Costs
Lesson 25: Advanced Troubleshooting and Debugging Techniques
25.01 Utilizing IBM Cloud Logging Services for Debugging
25.02 Analyzing Error Messages and Logs
25.03 Tracing Data Flow through the Monitoring Pipeline
25.04 Identifying and Resolving Connectivity Issues
25.05 Debugging Rule Engine Execution Problems
25.06 Troubleshooting Dashboard Loading and Data Display Issues
25.07 Utilizing API Debugging Tools
25.08 Implementing Proactive Error Detection
25.09 Best Practices for Effective Troubleshooting
25.10 Demonstration: Debugging a Complex Monitoring Issue
Lesson 26: Integrating Monitoring with DevOps Pipelines
26.01 Understanding the Role of Monitoring in DevOps
26.02 Implementing Infrastructure as Code for Monitoring Configuration
26.03 Automating Monitoring Deployment and Updates
26.04 Integrating Monitoring Alerts with CI/CD Pipelines
26.05 Utilizing Monitoring Data for Performance Testing
26.06 Implementing Automated Rollbacks Based on Monitoring Alerts
26.07 Monitoring the Health of Deployment Pipelines
26.08 Best Practices for Integrating Monitoring and DevOps
26.09 Troubleshooting DevOps Integration Issues
26.10 Demonstration: Integrating Monitoring into a Simple Deployment Pipeline
Lesson 27: Building Custom Monitoring Reports and Analytics
27.01 Utilizing IBM Cloud Databases for Reporting
27.02 Creating Custom SQL Queries for Monitoring Data
27.03 Integrating with Business Intelligence (BI) Tools (e.g., Cognos Analytics)
27.04 Generating Scheduled and On-Demand Reports
27.05 Visualizing Report Data with Charts and Graphs
27.06 Sharing and Distributing Monitoring Reports
27.07 Analyzing Report Data for Insights and Trends
27.08 Troubleshooting Reporting Issues
27.09 Best Practices for Report Design
27.10 Demonstration: Creating a Custom Monitoring Report
Lesson 28: Implementing Monitoring for Hybrid Cloud Environments
28.01 Understanding the Challenges of Monitoring Hybrid Clouds
28.02 Connecting On-Premises Devices to the IBM IoT Platform
28.03 Monitoring Devices in Different Cloud Environments
28.04 Aggregating Monitoring Data from Multiple Sources
28.05 Implementing Consistent Monitoring Policies Across Environments
28.06 Security Considerations for Hybrid Cloud Monitoring
28.07 Troubleshooting Connectivity Issues in Hybrid Environments
28.08 Best Practices for Hybrid Cloud Monitoring
28.09 Utilizing IBM Cloud Satellite for Edge and On-Premises Monitoring
28.10 Demonstration: Monitoring Devices in a Hybrid Cloud Setup
Lesson 29: Advanced Data Filtering and Transformation Techniques
29.01 Utilizing Advanced Filtering Expressions in Rules and Dashboards
29.02 Implementing Data Transformation Functions
29.03 Handling Different Data Types and Units
29.04 Aggregating and Summarizing Data for Monitoring
29.05 Implementing Data Enrichment Techniques
29.06 Utilizing Custom Functions for Data Processing
29.07 Troubleshooting Data Filtering and Transformation Issues
29.08 Performance Considerations for Data Processing
29.09 Best Practices for Efficient Data Handling
29.10 Demonstration: Implementing Advanced Data Filtering and Transformation
Lesson 30: Implementing Anomaly Detection for Proactive Monitoring
30.01 Understanding Different Anomaly Detection Techniques
30.02 Utilizing Statistical Methods for Anomaly Detection
30.03 Integrating with Machine Learning Services for Anomaly Detection
30.04 Configuring Rules Based on Anomaly Scores
30.05 Monitoring the Performance of Anomaly Detection Models
30.06 Handling False Positives and False Negatives
30.07 Visualizing Anomalies on Dashboards
30.08 Troubleshooting Anomaly Detection Issues
30.09 Best Practices for Implementing Anomaly Detection
30.10 Demonstration: Setting up Anomaly Detection for Device Data
Lesson 31: Monitoring User Experience and Application Performance
31.01 Understanding User Experience (UX) in IoT Applications
31.02 Monitoring Application Response Times and Latency
31.03 Tracking User Interactions and Flows
31.04 Identifying and Resolving Performance Bottlenecks
31.05 Utilizing Application Performance Monitoring (APM) Tools
31.06 Integrating Monitoring Data with APM Systems
31.07 Implementing Synthetic Monitoring for Application Availability
31.08 Troubleshooting Application Performance Issues
31.09 Best Practices for Monitoring User Experience
31.10 Demonstration: Monitoring an IoT Application's Performance
Lesson 32: Advanced Capacity Planning for Scalable Monitoring
32.01 Forecasting Future Monitoring Data Volume
32.02 Estimating Resource Requirements for Scalability
32.03 Identifying Potential Bottlenecks in a Growing System
32.04 Utilizing Load Testing and Stress Testing
32.05 Planning for Peak Monitoring Loads
32.06 Optimizing Resource Allocation for Cost and Performance
32.07 Monitoring Resource Utilization Trends
32.08 Troubleshooting Capacity-Related Issues
32.09 Best Practices for Capacity Planning
32.10 Demonstration: Planning for Future Monitoring Capacity
Lesson 33: Implementing Monitoring for IoT Solutions in Specific Verticals (Deep Dive)
33.01 Tailoring Monitoring Strategies for Manufacturing IoT
33.02 Specific Monitoring Needs for Energy and Utilities
33.03 Advanced Monitoring for Agriculture IoT
33.04 Monitoring Challenges in Transportation and Logistics
33.05 Implementing Healthcare-Specific Monitoring Requirements
33.06 Monitoring for Smart Buildings and Infrastructure
33.07 Addressing Unique Monitoring Needs in Retail
33.08 Case Studies of Vertical-Specific Monitoring Solutions
33.09 Best Practices for Vertical-Specific Monitoring
33.10 Demonstration: Applying Monitoring Concepts to a Specific Industry
Lesson 34: Advanced Security Event Monitoring and Response
34.01 Identifying and Monitoring for Security Events
34.02 Utilizing IBM Cloud Security Services for Monitoring
34.03 Implementing Intrusion Detection and Prevention
34.04 Configuring Alerts for Security Incidents
34.05 Developing a Security Incident Response Plan
34.06 Integrating Monitoring Data with Security Orchestration, Automation, and Response (SOAR) Platforms
34.07 Conducting Security Audits and Penetration Testing
34.08 Troubleshooting Security Monitoring Issues
34.09 Best Practices for Security Event Monitoring
34.10 Demonstration: Setting up Security Event Monitoring
Lesson 35: Monitoring for Data Quality and Integrity
35.01 Identifying Data Quality Issues in IoT Data
35.02 Implementing Data Validation Rules
35.03 Monitoring for Missing, Inaccurate, or Inconsistent Data
35.04 Utilizing Data Profiling Techniques
35.05 Implementing Data Cleansing and Transformation for Quality
35.06 Configuring Alerts for Data Quality Issues
35.07 Measuring Data Quality Over Time
35.08 Troubleshooting Data Quality Problems
35.09 Best Practices for Ensuring Data Quality
35.10 Demonstration: Monitoring for Data Quality Issues
Lesson 36: Advanced Integration with Cloud Native Technologies
36.01 Leveraging Kubernetes and Containers for Monitoring Applications
36.02 Utilizing Serverless Functions for Monitoring Tasks
36.03 Integrating with Cloud-Native Monitoring Tools (e.g., Prometheus, Grafana)
36.04 Implementing Microservices for Monitoring Components
36.05 Managing and Monitoring Cloud-Native Deployments
36.06 Utilizing API Gateways for Monitoring API Access
36.07 Security Considerations for Cloud-Native Monitoring
36.08 Troubleshooting Cloud-Native Integration Issues
36.09 Best Practices for Cloud-Native Monitoring
36.10 Demonstration: Integrating IBM IoT Monitoring with a Cloud-Native Application
Lesson 37: Implementing Monitoring for Regulatory Compliance (Advanced)
37.01 Deep Dive into Specific Regulatory Frameworks and their Monitoring Implications
37.02 Mapping Regulatory Requirements to Monitoring Controls
37.03 Implementing Automated Compliance Monitoring
37.04 Generating Compliance Reports from Monitoring Data
37.05 Integrating with Governance, Risk, and Compliance (GRC) Platforms
37.06 Responding to Compliance Violations Identified by Monitoring
37.07 Maintaining Audit Trails for Compliance
37.08 Troubleshooting Compliance Monitoring Failures
37.09 Best Practices for Advanced Regulatory Compliance Monitoring
37.10 Demonstration: Configuring Monitoring for a Complex Regulatory Requirement
Lesson 38: Advanced Troubleshooting Scenarios and Case Studies
38.01 Analyzing and Resolving Complex Connectivity Issues
38.02 Troubleshooting Performance Bottlenecks in High-Throughput Systems
38.03 Debugging Issues in Custom Monitoring Applications
38.04 Resolving Conflicts in Rule Engine Configurations
38.05 Troubleshooting Integration Issues with External Systems
38.06 Analyzing and Addressing Data Quality Problems
38.07 Investigating and Responding to Security Monitoring Alerts
38.08 Case Study: Troubleshooting a Large-Scale IoT Monitoring Deployment
38.09 Utilizing Advanced Debugging Tools and Techniques
38.10 Interactive Troubleshooting Exercises
Lesson 39: Future Trends in IoT Monitoring and IBM's Roadmap
39.01 Emerging Technologies in IoT Monitoring (AI, ML, Edge Analytics)
39.02 The Role of Digital Twins in Monitoring
39.03 Decentralized Monitoring Architectures
39.04 Enhanced Security Measures for Future Monitoring
39.05 The Impact of 5G and Low-Power Wide-Area Networks (LPWAN)
39.06 IBM's Vision and Roadmap for IoT Monitoring
39.07 New Features and Capabilities in the IBM IoT Platform
39.08 Preparing for Future Monitoring Challenges
39.09 Industry Trends and Best Practices
39.10 Q&A and Discussion on Future Trends
Lesson 40: Course Summary, Best Practices, and Next Steps
40.01 Recap of Key Concepts and Skills Learned
40.02 Review of Best Practices for Expert-Level IoT Monitoring
40.03 Summary of Advanced Configuration and Customization Techniques
40.04 Strategies for Continuous Improvement in Monitoring
40.05 Resources for Further Learning and Support
40.06 Certification Opportunities in IBM IoT
40.07 Building a Career in Advanced IoT Monitoring
40.08 Course Feedback and Evaluation
40.09 Final Q&A and Expert Panel Discussion