The presentation covers machine learning acceleration at scale, optimization of models, deployment to Kubernetes, and introduction of production cloud native tooling.
- Running ML server locally is important to ensure everything works and debug any issues before deployment to production.
- Other resources for CI/CD for production machine learning at scale, production machine learning monitoring, machine learning security, and machine learning ecosystem and operations.
- Collaboration with Hugging Face team to access a pre-trained GPT2 model using their Transformers library.
- Optimization of the model using ONNX serialization format.
- Deployment to Kubernetes cluster after testing locally to ensure it works.
- Anecdote about a computationally intensive dungeon crawler game that uses AI model for personalization.
The presentation mentions a dungeon crawler game where users can interact with an AI model to choose their own adventure. The game is computationally intensive and showcases the need for model optimization and acceleration.