MLOps is essential for companies both large and small that build products and services powered by AI. Given the wide variety of tools and platforms that aim to solve different parts of the machine learning lifecycle, choosing between them isn't always easy. Building a machine learning training and deployment pipeline is a fractured experience from the get-go. Below, we'll go through Lightning's unified platform for training and deploying machine learning models in production. Lightning (by the same people who built PyTorch Lightning) is a platform that augments the capabilities of PyTorch Lightning beyond training, into serving, deploying, monitoring, and data engineering.

With the release of PyTorch 1.12 in May of this year, PyTorch added experimental support for the Apple Silicon processors through the Metal Performance Shaders (MPS) backend. If you own an Apple computer with an M1 or M2 chip and have the latest version of PyTorch installed, you can now train models faster. Even better news: in the latest 1.7 release of PyTorch Lightning, we made it super easy to switch to the MPS backend without any code changes! Support for this accelerator is currently marked as experimental in PyTorch. Because many operators are still missing, you may run into a few rough edges. PyTorch and Lightning will continue to improve support for Apple Silicon, so stay tuned for future releases!

Learn enough Lightning to match the level of expertise required by your research or job. Researchers and machine learning engineers should start here. Add validation and test sets to avoid over/underfitting. Add parameters to your script so you can run from the commandline. Learn to scale up your models and enable collaborative model development at academic or industry research labs.

The 2022 Ford F-150 Lightning pickup has been revealed with a starting price under $40G. The all-electric truck is the most powerful F-150 ever and offers a range up to 300 miles per charge. April 26 marked the official start of production for the electric Ford F-150 Lighting and yet another milestone … in the rise of the robots. The F-150 Lightning is built at the Rouge Electric Vehicle Center. The F-150 Lightning is built with a unique assembly system in Ford's Rouge Electric Vehicle Factory in Dearborn where humans and robots really do work side by side.

Imagine a situation where you want to know the output shape from an operation. Typically, you would run the operation and check the size of the tensor after. With Meta Tensors, you don't have to compute the output to find the answer. Meta Tensors are just like normal tensors, except they have no data. In PyTorch meta is a device.

According to Aristotle, while living things moved themselves at will, inanimate things moved according to their natures: heavy things, made of earth or water, descended, while light things, made of air or fire, ascended. Twenty years later, the French King Henri IV hired the Italian engineer Tomaso Francini to build him some waterworks for the royal palace at Saint Germain en Laye. In 1650, the German polymath Athanasius Kircher offered an early design of a hydraulic organ with automata, governed by a pinned cylinder and including a dancing skeleton. The designers of the automatic loom used automata and automatic musical instruments as their model; then Charles Babbage -- the English mathematician who designed the first mechanical computers during the 1830s, the Analytical and Difference Engines -- in turn used the automatic loom as his model.