Summary

In this conversation, Serop Baghdadlian and Maria discuss the evolution of MLOps and AI technologies, and the importance of fundamentals in AI engineering. They explore the complexities of LLM Ops compared to traditional MLOps, the shift in evaluation standards for AI models, and the necessity of a data scientist's mindset when approaching AI projects. In this conversation, Serop Baghdadlian and Maria discuss the complexities and unpredictability of machine learning models, the evolution of MLOps, and the importance of focusing on fundamentals. They explore the challenges of reproducibility in machine learning environments, the journey of creating effective courses, and the significance of teaching and sharing knowledge in the tech community. Maria shares her experience in writing a book on MLOps with Databricks and emphasizes the need for simplicity in solutions.

Chapters

00:00 Coming Up

05:10 Navigating Complexity in AI Systems

09:50 Evaluating AI Models: The Shift in Standards

15:03 The Role of Human Oversight in AI

19:49 Building Reliable AI Systems

24:54 Teaching and Sharing Knowledge in AI

29:58 The Future of AI and Continuous Learning

Takeaways

Fundamentals in AI and MLOps are crucial and don't change rapidly.

Complex systems can lead to unreliability and financial loss.

MLOps focuses on principles rather than just tools.

Human oversight is essential in evaluating AI outputs.

Simplicity should be prioritized in building AI systems.

Teaching and sharing knowledge is vital for community growth.

Continuous learning is necessary due to the fast-paced nature of AI.

Evaluation standards for AI models have shifted towards gut feelings.

Collaboration and mentorship are important in the AI field.

Curiosity drives learning and understanding in AI.

Contacts

linkedin.com/in/maria-vechtomova

linkedin.com/in/serop-baghdadlian

#DataTales #DataScience #MLOps #AIEngineering #TechPodcast