With the newest iteration of its custom M1 chip, the M1 Pro and M1 Max versions, Apple has given the Machine Learning community a powerful tool. To some extent this power can only be unleashed if the system is set up correctly--despite Apple's user-friendliness, this is not a straightforward task.
Without a doubt, the ImageNet dataset has been a critical factor in developing advanced Machine Learning algorithms. Its sheer size and a large number of classes have been challenging to handle.
The specs are indeed intimidating: Up to 32 GPU cores and up to 16 CPU cores. Pair that with 64 GB of RAM, and you're well equipped for any workload. And, not to forget the design. Well, it seems Apple did it again.
Most of the time, we write and debug our code locally. After we've passed any tests, we then deploy the scripts to a remote environment. If we're fortunate, we might have access to multiple GPUs.
The ability to make machines learn is a fascinating achievement of the last decades. Many new business opportunities have opened up, and companies use Machine Learning on a day-to-day basis.
