Absolutely! The new M1 MacBook Air is an excellent choice for machine learning and deep learning tasks. Powered by Apple's impressive M1 chip, this laptop brings significant improvements in performance and efficiency.
While the M1 chip has been succeeded by the M2, it still offers fantastic performance, especially when compiling code. Sure, there's more powerful MacBooks out there, but none of them come close to providing the value that the M1 MacBook Air does.
The M1 Pro with 16 cores GPU is an upgrade to the M1 chip. It has double the GPU cores and more than double the memory bandwidth. You have access to tons of memory, as the memory is shared by the CPU and GPU, which is optimal for deep learning pipelines, as the tensors don't need to be moved from one device to another.
Sure, there's around 2x improvement in M1 than my other Intel-based Mac, but these still aren't machines made for deep learning. Don't get me wrong, you can use the MBP for any basic deep learning tasks, but there are better machines in the same price range if you'll do deep learning daily.
If you're looking for a high-performance laptop for AI and Machine Learning, then the Apple MacBook Air M1 Chip is a great option to consider. Apple M1 chip with an 8-core CPU delivers up to 3.5x faster performance than the previous generation while using way less power.
Apple MacBook Pro
This can be a great choice for the AI and ML engineers that comes with 256GB SSD storage, 8-Core GPU, M1 Chip with 8-Core CPU, and 8GB unified memory. The laptop also consists of a 16-Core neural engine along with a 33.74 cm Retina display with true tone.
Intel's i7 is faster than Apple's M1 when it comes to raw processing power. So, if you need a processor that can handle demanding tasks quickly, the i7 is the better option.
In the older ones I would suggest 16gb but on the M1 8 would be fine. It is because of the RAM and CPU (and GPU) being on the same chip. The closeness and speed is just fast almost as if it was CPU cache although not. Go for the 16 if you want but the 8 would be fine for programming.
MacBooks are great for Deep Learning. Again, the barrier to machine learning is RAM size, not your CPU. And, as we went over earlier, it's VERY cheap to lease out intense training to an online GPU system like Kaggle or Google.
11 trillion operations per second
The M1 chip brings the Apple Neural Engine to the Mac, greatly accelerating machine learning (ML) tasks. Featuring Apple's most advanced 16-core architecture capable of 11 trillion operations per second, the Neural Engine in M1 enables up to 15x faster machine learning performance.
Pros and Cons of The M1 for Data Science
Data science libraries such as TensorFlow and PyTorch benefit from more CPU cores, so the upgrade from 4 high-performance CPU cores in the original M1 to 8 in the new M1 Pro/Max will be definitely good for doing data science tasks.
If you're dealing with a modest quantity of data, an 8 GB computer can be plenty. However, 16 GB of RAM or more is recommended for larger data sets, and large amounts of RAM are often required for machine learning systems to store and process massive datasets.
Intel's i7 is faster than Apple's M1 when it comes to raw processing power. So, if you need a processor that can handle demanding tasks quickly, the i7 is the better option. Apple's M1 is more energy-efficient than Intel's i7, so it will run cooler and last longer on a single charge.
How fast is M1 compared to i9 Both processors are fairly speedy. However, in most tests conducted, the i9 processors come out on top. That's not to say the M1 isn't speedy – it's got a clock frequency of 3.2Hz, which should be fine for most tasks.
The M2 is better than the M1, but it's still a marginal upgrade. The M1 Pro is similarly topped by the M2 Pro, and the M2 Max beats out the M1 Max in pretty much every test.
The golden rule is that you will never regret having more RAM. The more you have, the smoother your programming and overall computer usage experience will be, and the more you can run at once. However, if you are on a budget, a computer with 8GB or 16GB should be more than enough for programming.
Apple Laptops are great for Machine Learning. While the M2 chip is a home run, the older intel processors were still really good. If you're going to get an older Apple laptop for getting into machine learning and want to save some money on your purchase, emphasize RAM (16GB) over the upgraded CPUs.
If you're looking for a laptop that can handle typical data science workloads and doesn't scream cheap plastic and unnecessary red details, M1 might be the best option. It's fast, responsive, light, has a superb screen, and all-day battery life. Plus, you can definitely use it for data science.
Python installed by
Miniforge-arm64, so that python is natively run on M1 Max Chip. (Check from Activity Monitor, Kind of python process is Apple ). Anaconda.: Then python is run via Rosseta.
The larger the RAM the higher the amount of data it can handle hence faster processing. With larger RAM you can use your machine to perform other tasks as the model trains. Although a minimum of 8GB RAM can do the job, 16GB RAM and above is recommended for most deep learning tasks.
AI Hardware requirement
That typically includes a CPU with 8 cores, 32 GB RAM, 1 TB hard drive, and an NVIDIA GeForce RTX 1080 (or 2080) Series 8GB GPU. Computers with FPGAs: Field Programmable Gate Array (FPGA) uses in AI. Altera and Xilinx are the two most well-known FPGA manufacturers.
The Intel i7 is faster than the Apple M1, making it better suited for tasks that require a lot of processing power. However, the Apple M1 is more energy-efficient, meaning it will save you money on your energy bill.
At the time of introduction in 2020, Apple said that the M1 had the world's fastest CPU core "in low power silicon" and the world's best CPU performance per watt. Its successor, Apple M2, was announced on June 6, 2022 at WWDC. Apple Inc.
BREAKING: We've just discovered that the base 14” M2 Pro MacBook Pro (512GB) is considerably slower than the previous 14” M1 Pro model. Apple is likely using single SSD modules again (like the base 256GB M2 Air and M2 MacBook Pro).
Performance and battery life
Apple says the M2 chip is up to 1.4 times faster than the previous M1 model while still getting up to 18 hours of battery life. While Apple doesn't advertise a huge jump in battery life with the M2 MacBook Air, in our testing, the latest model was significantly longer-lasting.