Running Self-Hosted Models with Ruby and Chris Hasinski

Send us Fan Mail In this episode of the Ruby AI Podcast, hosts Valentino Stoll and Joe Leo welcome AI and Ruby expert Chris Hasinski. They delve into the benefits and challenges of self-hosting AI models, including control over model updates, cost considerations, and the ability to fine-tune models. Chris shares his journey from machine learning at UC Davis to his extensive work in AI and Ruby, touching upon his contributions to open source projects and the Ruby AI community. The discussion also covers the limitations of current LLMs (Large Language Models) in generating Ruby code, the importance of high-quality data for effective AI, and the potential for Ruby to become a strong contender in AI development. Whether you're a Ruby enthusiast or interested in the intersection of AI and software development, this episode offers valuable insights and practical advice. 00:00 Introduction and Guest Welcome 00:31 Why Self-Host Models? 01:28 Challenges and Benefits of Self-Hosting 03:14 Chris's Background in Machine Learning 04:13 Applications Beyond Text 06:39 Fine-Tuning Models 12:27 Ruby in Machine Learning 16:06 Distributed Training and Model Porting 18:22 Choosing and Deploying Models 25:19 Testing and Data Engineering in Ruby 27:56 Database Naming Conventions in Different Languages 28:19 Importance of Data Quality for AI 18:03 Monitoring Locally Hosted AI Models 29:37 Challenges with LLMs and Performance Tracking 31:09 Improving Developer Experience in Ruby 31:45 Ruby's Ecosystem for Machine Learning 32:43 The Need for Investment in Ruby's AI Tools 38:25 Challenges with AI Code Generation in Ruby 43:35 Future Prospects for Ruby in AI 51:26 Conclusion and Final Thoughts