↕️↔️TensorFlow 2.10 is Here
Weekly news digest curated by the industry insiders
TensorFlow and PyTorch have become the two most popular deep learning frameworks within the data science community. Their dominance in the market is strong. And it increases as both frameworks have been very fast to incorporate cutting-edge deep learning methods and ML engineering techniques that can help accelerate the implementation of deep learning solutions. As a result, each release of these frameworks drives a lot of attention across the data science space. Last week was TensorFlow’s turn with the release of its 2.10 version.
TensorFlow 2.10 shouldn’t be considered a major release but certainly incorporates many features that have been highly demanded by the developer community. An improved experience for Keras developers, wider hardware topology coverage, and improved libraries were at the center of this release. As usual, the frantic pace of innovation in TensorFlow and PyTorch does not disappoint.
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Edge#225: we explain latent diffusion models; discuss the original latent diffusion paper; explore Hugging Face Diffusers, a library for state-of-the-art diffusion models.
Edge#226: we deep dive DeepSpeed Compression, a new library for extreme compression of deep learning models
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Now, let’s review the most important developments in the AI industry this week
🔎 ML Research
Conversational Agents and Human Values
DeepMind published a fascinating research paper drawing lessons from philosophy and linguistics to improve the alignment of conversational agents and human values →read more
Fairness in Connection Recommender Systems
Researchers from Carnegie Mellon University (CMU) published a paper outlining fairness techniques for connection recommender systems which are common in social networking platforms →read more
Auto Scheduler Optimizations
Amazon Research published a paper introducing DietCode, an auto-scheduler method that can drastically optimize tensor operations in deep learning models →read more
Game Theory and Offline RL
Microsoft Research published two papers detailing a game theoretic approach to improve offline reinforcement learning models →read more
Better Transformers for Computer Vision
Google Research published a paper detailing a multi-axis technique to improve computer vision transformer architectures →read more
📌 Event: Learn strategies to scale your ML models using Kubernetes - SEP 14
Get ahead of the curve, and learn practical hands-on guidance from Kubernetes expert Itay Ariel on how to leverage Kubernetes for distributed workloads. Itay will give an overview of the unique challenges of scaling workloads and show how to leverage Kubernetes to easily scale your ML models and automate the management of workload performance.
🤖 Cool AI Tech Releases
A new version of TensorFlow has been related with an exciting set of capabilities →read more
MLCommons unveiled a new series of MLPerf benchmarks that evaluate the performance of inference models →read more
🛠 Real World ML
MLOps at Walmart
Walmart discusses some insightful details about the MLOps infrastructure and processes powering their ML pipelines →read more
💸 Money in AI