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.

If you are a software engineer, there's a good chance that deep learning will inevitably become part of your job in the future. Even if you're not building the models that directly use CNNs, you might have to collaborate with data scientists or help business partners better understand what is going on under the hood. In this article, Julie Kent dives into the world of convolutional neural networks and explains it all in a not-so-scary way.

SEO is continually evolving and the techniques and strategies that SEO experts were using a few years back are no longer valid.

Many enterprise data science teams are using Cloudera’s machine learning platform for model exploration and training, including the creation of deep learning models using Tensorflow, PyTorch, and more. However, training a deep learning model is often a time-consuming process, thus GPU and distributed model training approaches are employed to accelerate the training speed.

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.