Summary
This conversation explores the rapid evolution of AI, the complexities of AI roles, the importance of MLOps in deployment, and the challenges faced in implementing AI projects. The speakers share their personal journeys in AI engineering, discuss the balance between custom models and APIs, and emphasize the need for effective data retrieval methods in AI applications. In this conversation, the speakers delve into the complexities of AI, particularly in the context of MLOps and generative AI. They discuss the challenges of ambiguity in AI queries, the evolution of best practices in MLOps, and the importance of evaluation in AI models. The conversation also touches on the transition to content creation, the European AI landscape, and predictions for the future of AI, including advancements in robotics and genomics.
Chapters
00:00 Coming Up
03:07 The Journey into AI Engineering
05:55 The Complexity of AI Roles
08:56 Custom Models vs. APIs in AI
11:52 The Role of MLOps in AI Deployment
15:00 Challenges in AI Project Implementation
18:11 Building Production-Ready AI Systems
20:49 Integrating Semantic Search in AI Applications
28:19 Navigating Ambiguity in AI Queries
30:37 The Evolution of MLOps in the Age of Gen AI
32:39 The Challenges of Evaluation in AI Models
35:41 Evaluating Non-Deterministic AI Systems
38:58 Transitioning to Full-Time Content Creation
41:35 The European AI Landscape and Data Security
44:14 Staying Updated in a Rapidly Evolving Field
48:15 Predictions for the Future of AI
Takeaways
AI is evolving rapidly, making it challenging to keep up.
Europe has a strong advantage in data security for AI.
The journey into AI can be overwhelming due to its complexity.
Custom models may be necessary for specific tasks to reduce costs.
MLOps is crucial for deploying AI systems effectively.
Many AI projects fail due to unrealistic expectations and lack of resources.
Building production-ready AI systems requires careful planning and organization.
Semantic search can enhance data retrieval in AI applications.
Understanding user intent is key to effective AI solutions.
Collaboration and communication are essential in AI project success. Navigating ambiguity in AI queries is challenging but essential.
MLOps principles are being overlooked in the rush of Gen AI.
Evaluation of AI models is crucial and often neglected.
Non-deterministic AI systems require careful evaluation.
Transitioning to content creation can lead to a disconnect from industry practices.
The European AI landscape is rich with talent and innovation.
Data security is a competitive advantage for European companies.
Staying updated in AI requires a strategic approach to information consumption.
Robotics and genomics are poised to be the next big advancements in AI.
Trusting oneself to learn and adapt is crucial in the evolving AI landscape.
Contact
linkedin.com/in/serop-baghdadlian
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