AI and the Future of Work: Workflows and Modern Tools for Tech Leaders

Learn how to implement machine learning operations into your business, discover the scope of what ML can achieve—and the challenges that it can present.

Introduction

• Welcome
• Why is this important?

1. Streamlining AI Workflows with Modern Tools

• Data versioning and management
• Experiment tracking and management
• Model monitoring and performance evaluation
• AutoML
• Automated pipelines
• Explainability and interpretability of models
• Model deployment and serving
• Tools for working with LLMs

2. Developing Future-Ready AI Skills

• Assessing and upskilling existing teams
• Navigating the hybrid skill set landscape
• Creating an environment for experimentation
• Emerging trends in how you build AI
• Challenges and opportunities for organizations

Conclusion

• Recap of key takeaways
• Actionable insights for implementing best practices