This blog post is a first of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML / DL project. The first part of this blog describes common problems appearing when developing ML / DL solutions, and the second describes a simple image classification example demonstrating how to use Allegro Trains and PyTorch to address those problems.
If you write software for a living, you probably have a bias toward coded tests and against all things codeless. Most software engineers who become test engineers trust themselves to write well-designed structured code. Some people see record-and-playback as cheating, demeaning, or otherwise indicative of poor workmanship. Yet, research shows that test code maintenance costs correlate directly to the number of lines of written test code.
Every researcher or machine learning enthusiast faces that well-known experiment management nightmare; it’s usually a rude awakening discovered at the beginning of one’s career. Here’s how it goes.
The resurrection of AI due to the drastic increase in computing power has allowed its loyal enthusiasts, casual spectators, and experts alike to experiment with ideas that were pure fantasies a mere two decades ago. The biggest benefactor of this explosion in computing power and ungodly amounts of datasets (thank you, internet!) is none other than deep learning, the sub-field of machine learning(ML) tasked with extracting underlining features, patterns, and identifying cat images.
Over the last 100 years alone, artificial intelligence has achieved what was once believed to be science fiction: cars that drive themselves, machine learning models that diagnose heart disease better than doctors can, and predictive customer analytics that lead to companies knowing their customers better than their parents do. This machine learning revolution was sparked by a simple question: can a computer learn without explicitly being told how?
Trigo is a provider of AI & computer vision based checkout-free systems for the retail market, enabling frictionless checkout and a range of other in-store operational and marketing solutions such as predictive inventory management, security and fraud prevention, pricing optimization and event-driven marketing.
In addition to the very real negative impact on every person around the world, the COVID-19 pandemic is driving business disruptions and closures at an unprecedented scale. Enormous government stimulus programs are resulting in explosions in fiscal deficits, regulators are relaxing capital constraints on banks and central banks are supporting economic stability with a range of interest rate cuts and other stimulus measures.
We’re excited to introduce v 0.15 of Allegro Trains. With this version we’ve taken Trains one step further to provide even more powerful features for the community to manage their AI workloads.
There’s a lot to track when training your ML models, and there’s no way around it; reviews and comparisons for best performance are virtually impossible without logging each experiment in detail. Yes, building models and experimenting with them is exciting work, but let’s agree that all that documentation can be laborious and error-prone – especially when you are essentially doing data entry grunt work, manually, using Excel spreadsheets.
