Summary of M1 Max/Pro vs M1 Macbook for Data Science.... should you?!?

00:00:00 - 00:15:00

The M1 Max is the best chip for data science, outperforming the M1 Pro and M1 Macbook. However, the M1 Max is not as fast as the Tesla V100. The Macbook Pro is a better choice than the Macbook Air for data science, as it has a 10 core CPU and supports dual monitors.

• 00:00:00 The author of the video discusses how the new M1 Max and M1 Pro chips outperform the older M1 Macbook in various programming languages, machine learning tools, and virtualization software. He does not mention any specific issues with the M1 Max or Pro.
• 00:05:00 The YouTube video discusses the pros and cons of the different M1 chips - the M1 Max vs M1 Pro vs M1 Macbook - when it comes to data science. The M1 Max performs better than the M1 Pro and M1 Macbook, but the M1 Max is not as fast as the Tesla V100. Tableau and Power BI are both fully supported on the M1 chips, though using Power BI on the M1 Mac may be slightly laggy. For virtual machines, the M1 Max is the most stable option.
• 00:10:00 The author of the video compares the performance of the Macbook Air with the Macbook Pro with respect to data science. He finds that the Macbook Air is faster than the Macbook Pro, but that the Macbook Pro has more features, such as a touch bar, which may warrant the extra cost. He recommends the Macbook Air for people who are starting out in data science, or who don't need the features the Macbook Pro has.
• 00:15:00 The author recommends that people consider the Macbook Pro over the Macbook Air when it comes to data science, as the Macbook Pro has a 10 core CPU instead of the 8 core CPU on the Macbook Air. Additionally, the Macbook Pro supports dual monitors, whereas the Macbook Air does not. The author also recommends that people have at least 512 GB of storage for their computer, in case they need to store data outside of their computer.