Artificial Intelligence / Machine Learning System Safety Workshop by Tonex
This comprehensive workshop, delivered by Tonex, provides an in-depth exploration of the critical aspects of ensuring safety in Artificial Intelligence (AI) and Machine Learning (ML) systems. Participants will gain a profound understanding of the potential risks associated with AI/ML applications and learn how to design, deploy, and maintain safe AI systems. The course covers essential topics like data quality, model validation, risk assessment, and compliance considerations. With a focus on practical applications and industry best practices, attendees will leave with the knowledge and tools to create AI/ML systems that are safe, reliable, and compliant.
Learning Objectives: Upon completing this workshop, participants will be able to:
- Identify potential safety risks and challenges in AI/ML systems.
- Understand best practices for data quality, including data collection, cleansing, and labeling.
- Apply model validation techniques to ensure the reliability of AI/ML algorithms.
- Conduct risk assessments and mitigation strategies for AI/ML projects.
- Comprehend regulatory and compliance considerations in the AI/ML space.
- Develop strategies for ongoing safety monitoring and maintenance of AI/ML systems.
Audience: This workshop is designed for professionals, engineers, data scientists, and project managers working in AI/ML development across various industries. It is ideal for individuals involved in designing, deploying, or maintaining AI systems and seeking to enhance their understanding of safety and compliance considerations.
Course Outline:
Introduction to AI/ML System Safety
- Defining AI/ML system safety
- The importance of safety in AI/ML
- Safety challenges and risks in AI/ML
Data Quality for AI/ML Safety
- Data collection best practices
- Data cleansing and preprocessing
- Data labeling and annotation
- Ensuring data integrity and accuracy
Model Validation and Reliability
- Model selection and evaluation
- Cross-validation techniques
- Ensuring robustness and reliability of AI models
- Dealing with overfitting and underfitting
Risk Assessment and Mitigation
- Identifying risks in AI/ML projects
- Quantifying and prioritizing risks
- Risk mitigation strategies and planning
- Case studies in risk assessment
Regulatory and Compliance Considerations
- AI/ML regulations and standards
- Compliance with data privacy laws
- Ethical considerations in AI/ML
- Navigating industry-specific regulations
Ongoing Safety Monitoring and Maintenance
- Implementing continuous monitoring
- Identifying safety degradation
- Strategies for system maintenance
- Preparing for unexpected events and failures
Participants will leave this workshop with the knowledge and tools to ensure safety in AI/ML systems and the ability to apply these concepts to their own projects, ultimately contributing to the responsible and ethical advancement of artificial intelligence and machine learning technologies.