Session 3 AI Technologies & Applications
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Session 3

AI Technologies & Applications

Mastering the AI Technology Landscape

CEO AI Mentor Program - Torsten's Technology Journey

Duration: 55-70 minutes

AI Technology Stack

Application Layer

Chatbots & Virtual Assistants
Recommendation Systems
Predictive Analytics
Computer Vision Apps

AI/ML Models

Machine Learning Algorithms
Deep Learning Networks
Natural Language Processing
Computer Vision Models

AI Platforms

MLOps Platforms
Model Training Infrastructure
AutoML Tools
Model Serving Systems

Data Infrastructure

Data Lakes & Warehouses
ETL/ELT Pipelines
Real-time Streaming
Data Governance

Cloud Infrastructure

Compute Resources
Storage Systems
Networking
Security & Compliance

Industry-Specific AI Applications

Retail & E-commerce

Personalized Recommendations +35% conversion
Dynamic Pricing +20% margins
Inventory Optimization -25% waste

Manufacturing

Predictive Maintenance -40% downtime
Quality Control +95% accuracy
Supply Chain AI -30% costs

Financial Services

Risk Assessment +90% accuracy
Fraud Detection +99% detection
Algorithmic Trading +25% returns

Healthcare

Diagnostic Imaging +95% accuracy
Drug Discovery -50% time
Patient Monitoring +60% efficiency

AI Vendor Selection Criteria

Technical Capabilities

Model Performance 25%
Scalability 20%
Integration Ease 15%
API Quality 10%

Security & Compliance

Data Security 20%
Compliance Standards 15%
Audit Capabilities 10%
Data Privacy 15%

Business Factors

Cost & ROI 25%
Support Quality 20%
Vendor Stability 15%
Innovation Roadmap 10%

Vendor Selection Process

1 Define Requirements
2 Research Vendors
3 Evaluate Solutions
4 Pilot Testing
5 Final Decision

AI Integration Strategies

API Integration

Advantages:

  • Quick implementation
  • No infrastructure needed
  • Regular updates included
  • Pay-per-use model

Considerations:

  • Data privacy concerns
  • Internet dependency
  • Limited customization
  • Ongoing costs

On-Premise Deployment

Advantages:

  • Full data control
  • Customization flexibility
  • No internet dependency
  • Regulatory compliance

Considerations:

  • Higher upfront costs
  • Infrastructure requirements
  • Maintenance responsibility
  • Scalability challenges

Hybrid Cloud

Advantages:

  • Best of both worlds
  • Scalability benefits
  • Data security options
  • Cost optimization

Considerations:

  • Complex architecture
  • Integration challenges
  • Management overhead
  • Security complexity

Integration Best Practices

Start with pilot projects
Ensure data quality
Plan for scalability
Monitor performance
Train users effectively
Maintain security standards

Emerging AI Technology Trends

Session 3 Summary

Technology Mastery

Technology Stack: Understanding the complete AI technology ecosystem

Industry Applications: Real-world AI use cases across sectors

Vendor Selection: Criteria and process for choosing AI solutions

Integration Strategies: Best practices for AI implementation

Your Action Items

1 Map your technology stack requirements
2 Research relevant industry applications
3 Evaluate vendor options
4 Prepare for Session 4: Data Strategy

Session 3 Complete!

You've mastered the AI technology landscape

Coming Up: Session 4

Data Strategy & Analytics

Data Strategy Analytics Capabilities Data Governance Data-Driven Culture
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