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

1h 5mGeneral2024-01-12

Authors

Kristen Kehrer

Course details

“Putting machine learning into production" has a very different definition in 2023 than it did in 2013. The scope of what is possible to achieve with ML has increased significantly, and the associated challenges have grown proportionally. To effectively lead a data science team, you need to understand what you can feasibly expect them to achieve given the current state of ML, what challenges they will inevitably face, and how to navigate the modern ML ecosystem to provide them with support. In this course, instructor Kristen Kehrer teaches you all three. Learn how to streamline AI workflows with modern tools, manage AI projects and teams, collaborate with technical experts. Plus, develop future-proof leadership skills and keep abreast of the latest advancements, trends, and best practices.

Skills covered

Leadership SkillsArtificial Intelligence FoundationsArtificial Intelligence (AI)Leadership and ManagementOne-Off

Concepts

0. Introduction

01 - Welcome
02 - Why is this important

1. Streamlining AI Workflows with Modern Tools

03 - Data versioning and management
04 - Experiment tracking and management
05 - Model monitoring and performance evaluation
06 - AutoML
07 - Automated pipelines
08 - Explainability and interpretability of models
09 - Model deployment and serving
10 - Tools for working with LLMs

2. Developing Future-Ready AI Skills

11 - Assessing and upskilling existing teams
12 - Navigating the hybrid skill set landscape
13 - Creating an environment for experimentation
14 - Emerging trends in how you build AI
15 - Challenges and opportunities for organizations

Conclusion

16 - Recap of key takeaways
17 - Actionable insights for implementing best practices