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Course Outline
Introduction to Edge AI in Financial Services
- An overview of Edge AI and its specific applications in the finance sector.
- Analyzing the advantages and challenges of adopting Edge AI in banking.
- Examination of case studies highlighting successful Edge AI implementations in finance.
Setting Up the Edge AI Environment
- Installation and configuration of essential Edge AI tools.
- Integration of financial data sources and collection systems.
- Introduction to key Edge AI frameworks and libraries.
- Practical exercises focused on environment setup.
Fraud Detection with Edge AI
- Foundational concepts of fraud detection.
- Development of AI models for real-time fraud identification.
- Implementation of advanced anomaly detection systems.
- Hands-on practice in building fraud detection logic.
Enhancing Customer Service Using Edge AI
- Contextualizing customer service within the financial industry.
- Applying AI techniques for personalized client interactions.
- Deployment of AI-driven chatbots and virtual assistants.
- Practical exercises in developing customer service applications.
Risk Management with Edge AI
- Overview of risk management principles.
- Utilizing AI for real-time risk assessment and mitigation strategies.
- Creating AI-driven decision support systems.
- Applied exercises in risk management workflows.
Deploying and Managing Edge AI Solutions
- Deploying AI models on dedicated financial edge devices.
- Strategies for monitoring and maintaining Edge AI systems.
- Techniques for troubleshooting and optimizing deployed models.
- Hands-on tasks covering deployment and operational management.
Tools and Frameworks for Financial Edge AI
- Survey of relevant tools and frameworks, such as TensorFlow Lite and OpenVINO.
- Application of TensorFlow Lite in financial AI contexts.
- Practical work with optimization tools for performance enhancement.
Real-World Applications and Case Studies
- Analysis of successful financial Edge AI projects.
- Discussion of industry-specific use cases and best practices.
- Capstone project: Building and optimizing a realistic financial AI application.
Summary and Next Steps
Requirements
- A solid understanding of core AI and machine learning concepts.
- Familiarity with financial services and fintech application ecosystems.
- Foundational programming skills, with Python recommended for optimal outcomes.
Target Audience
- Professionals in the finance industry.
- Developers specializing in fintech solutions.
- AI specialists and data scientists.
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
