The war of words among AI supercomputer vendors escalated this week with Google claiming that its TPU-based system is faster and more efficient than Nvidia's A100-based entry, according to its own testing. Nvidia countered that its H100 system is faster based on testing conducted by the independent MLCommons using MLPerf 3.0. Google researchers reported that its Tensor Processing Unit-based supercomputer v4 is 1.2 to 1.7 times faster than Nvidia's 3-year-old A100 system and uses between 1.3 to 1.9 times less power. The MLPerf 3.0 benchmarks measured Nvidia's newer H100 against systems entered by 25 organizations, but Google's TPU-based v4 system was not one of them. A direct system-to-system comparison of the two companies' latest systems would have to be conducted by an independent organization running a variety of AI-based workloads for any benchmarks to be definitive, analysts said.

As you might have guessed, these courses aren't necessarily the best way for someone without a technical background to break into a career as a data scientist or machine learning engineer. From a career perspective, these courses seem to be most useful for allowing anyone who already has a degree in a technical subject, such as maths, computer science or engineering to specialise and build on their technical foundations.