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Sponsor Presentation - Johannes Messner: Modern, typed Python for (multimodal) ML
Discover how modern, typed Python enables machine learning with multimodal data using advancements in type hinting, Pydantic, FastAPI, and more.
- Modern, typed Python for machine learning is possible with advancements in type hinting, making it easier to work with multimodal data.
- Pydantic and FastAPI allow for type-safe APIs and automatically coerce types, reducing the risk of errors.
- The Python ecosystem is rapidly evolving to support type hinting, with tools like MyPy and Typer, making it easier to write type-safe code.
- Vector databases like Qdrant, Elasticsearch, and HNSWlib are well-suited for storing and querying vector data, and can be integrated with Python machine learning models.
- The duck test principle can be applied to ensure type-safety, where a type is not just defined by its shape, but by its behavior.
- Docker 8 supports generics and can be used to build vector databases, making it a valuable tool for machine learning applications.
- When working with multimodal data, it is often necessary to use multiple libraries and frameworks, making it important to have a clear understanding of the relationships between them.
- Typing can help reduce errors and make code more maintainable, especially in machine learning applications where data types can be complex and nuanced.