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.
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.