The array API standard is unifying the ecosystem of Python array computing, facilitating greater interoperability between code written for different array libraries, including NumPy, CuPy, PyTorch, JAX, and Dask.

But what are all of these "array-api-" libraries for? How can you use these libraries to 'future-proof' your* libraries, and provide support for GPU and distributed arrays to your users? Find out in this talk, where I'll guide you through every corner of the array API standard ecosystem, explaining how SciPy and scikit-learn are using all of these tools to adopt the standard. I'll also be sharing progress updates from the past year, to give you a clear picture of where we are now, and what the future holds.

Work with quantities (values with units) in Python? Come and help brainstorm ideas and voice your opinions for standardised APIs!

Discussion session for https://github.com/quantity-dev/metrology-apis and related efforts.