Explosion

DECEMBER 30, 2021

The year 2021 is coming to an end, and like the previous year, it was shaped by unique challenges that impacted our work together. For Explosion, it was a very productive year. Jan 22: Ines was invited as a guest to the TalkPython podcast and discussed how to build a data science startup. Mar 4: We released 1.0

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Python 52

Flipboard

MARCH 27, 2023

The success of PyTorch is attributed to its simplicity, first-class Python integration, and imperative style of programming. Since the launch of PyTorch in 2017, it has strived for high performance and eager execution. It has provided some of the best abstractions for distributed training, data loading, and automatic differentiation.

Deep Learning 117

Deep Learning Deep Learning Python Natural Language Processing 117

AWS Machine Learning Blog

JANUARY 13, 2023

This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Because they are in a highly regulated domain, HCLS partners and customers seek privacy-preserving mechanisms to manage and analyze large-scale, distributed, and sensitive data. Background.

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Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. When a task exceeds a foundation model’s capabilities, it can return an incorrect and fabricated “ hallucination ” that appears as plausible as a correct response. it will select a variation of “is.”

Natural Language Processing 83

Natural Language Processing Supervised Learning Machine Learning Machine Learning 83

AWS Machine Learning Blog

FEBRUARY 28, 2023

In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. It enables them to unlock the value of their data, identify trends, patterns, and predictions, and differentiate themselves from their competitors.

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ML ML AWS Machine Learning 71

AWS Machine Learning Blog

APRIL 17, 2023

We begin by discussing different types of model optimizations that can be used to boost performance before you deploy your model. At a high level, partitioning (with kernel optimization) brings down inference latency up to 66% (for example, BLOOM-176B from 30 seconds to 10 seconds), compilation by 20%, and compression by 50% (fp32 to fp16).

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

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