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.

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.

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.

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.

May 14, 2020 — Allegro AI today announced that it joined the NVIDIA DGX-Ready Software program. Organizations that want to leverage AI to improve products and services often struggle to implement an advanced infrastructure that supports the unique and challenging demands of machine learning and deep learning.