Data Phoenix Digest - ISSUE 52

DALL-E is now available in beta, and DALL-E 2 prompt book, introduction to diffusion models for ML, distribute your PyTorch model in less than 20 lines of code, k-means mask transformer, explaining chest X-ray pathologies in natural language, FIGS, NU-Wave, VQAD, datasets, tools, courses, and more.

Dmitry Spodarets
Dmitry Spodarets


By answering some questions related to your experience, skills, and toolset, you will help us determine the industry's state in 2022 and prepare the report.


Track your ML experiments end to end with Data Version Control and Amazon SageMaker Experiments
This post walks you through an example of how to track experiments across code, data, artifacts, and metrics by using Amazon SageMaker Experiments and Data Version Control (DVC).

Text Embeddings Visually Explained
Text Embeddings allow you to turn unstructured text data into a structured form. In this post, you’ll learn about their basics, use cases, customizations, and finetuning. Check it out!

Introduction to Diffusion Models for Machine Learning
Diffusion models are a conceptually simple and elegant approach to the problem of generating data. In this guide, you’ll learn everything you need to know about them.

Distribute Your PyTorch Model in Less Than 20 Lines of Code
In this guide, you’ll find out how to distribute a minimal training pipeline on more than one GPU. A simple, practical guide with only 15 lines of code to distribute your pipeline.

Inside NLLB-200, Meta AI’ New Super Model that Achieved New Milestones in Machine Translations Across 200 Languages
Meta AI’s NLLB-200 is one of the most impressive attempts to make AI more inclusive. It achieved impressive milestones outperforming state-of-the-art models in both 100 and 200 languages.

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