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Ines Montani at QCon London: Economies of Scale Can’t Monopolise the AI Revolution
During her presentation at QCon London, Ines Montani, co-founder and CEO of explosion.ai (the maker of spaCy), stated that economies of scale are not enough to create monopolies in the AI space and that open-source techniques and models will allow everybody to keep up with the “Gen AI revolution”.
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Airbnb Open-Sources its ML Feature Platform Chronon
Chronon, Airbnb's platform which creates the infrastructure required to transform raw data into ML-ready features, is now open source. As Airbnb ML infrastructure engineer Varant Zanoyan explains, Chronon supports a variety of data sources and aims to provide low-latency streaming.
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Canonical Launches Charmed MLFlow to Simplify Management and Maintenance of ML Workflows
Based on the open-source MLflow platform, Canonical Charmed MLFlow aims to simplify the task of managing machine learning workflows and artifacts by using alternative packaging system and orchestration engine.
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Google Uses AutoML to Discover More Efficient AI Training Algorithm
Researchers at Google have open-sourced EvoLved sIgn mOmeNtum (Lion), an optimization algorithm for training neural networks, which was discovered using an automated machine learning (AutoML) evolutionary algorithm. Models trained with Lion can achieve better accuracy on several benchmarks than models trained with other optimizers, while requiring fewer compute cycles to converge.
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ML.NET 2.0 Release Contains New NLP APIs and AutoML Updates
Microsoft announced the release of ML.NET 2.0, the open-source machine learning framework for .NET. The release contains several updated natural language processing (NLP) APIs, including Tokenizers, Text Classification, and Sentence Similarity, as well as improved automated ML (AutoML) features.
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How GPT3 Architecture Enhanced AI Capabilities: Lifearchitect.ai Keynote At Devoxx
Dr. Alan D. Thompson, the man behind lifearchitect.ai, sees the current AI trajectory as a shift more profound than the discovery of fire, or the WWW. His Devoxx keynote presents the state of the AI industry, following Google’s Transformer architecture introduction, a true transformer of the industry that gave rise to new AI models, which can conceptualize images, books from scratch and much more.
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LinkedIn Open-Sourced Its Feature Store to Evangelize Productive Machine Learning
LinkedIn Engineering recently open-sourced its feature store Feathr, which helps engineers to develop machine Learning products by simplifying feature management and usage in production. It defines features, computes them for training and inference purposes, and makes them discoverable by other machine learning developers.
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Amazon SageMaker Serverless Inference Now Generally Available
Amazon recently announced that SageMaker Serverless Inference is generally available. Designed for workloads with intermittent or infrequent traffic patterns, the new option provisions and scales compute capacity according to the volume of inference requests the model receives.
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Alibaba Open-Sources AutoML Algorithm KNAS
Researchers from Alibaba Group and Peking University have open-sourced Kernel Neural Architecture Search (KNAS), an efficient automated machine learning (AutoML) algorithm that can evaluate proposed architectures without training. KNAS uses a gradient kernel as a proxy for model quality, and uses an order of magnitude less compute power than baseline methods.
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Meta AI’s Convolution Networks Upgrade Improves Image Classification
Meta AI released a new generation of improved Convolution Networks, achieving state-of-the-art performance of 87.8% accuracy on Image-Net top-1 dataset and outperforming Swin Transformers on COCO dataset where object detection performance is evaluated. The new design and training approach is inspired by the Swin Transformers model.
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Google Open-Sources AutoML Algorithm Model Search
A team from Google Research has open-sourced Model Search, an automated machine learning (AutoML) platform for designing deep-learning models. Experimental results show that the system produces models that outperform the best human-designed models, with fewer training iterations and model parameters.
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Udacity and Microsoft Launch ML Engineer on Azure Course
Microsoft and Udacity have joined forces to launch a machine learning (ML) engineer training program focused on training, validating, and deploying models using the Azure Suite. The program is open to students with minimal coding experience and will focus on using Azure automated ML.
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Google Announces Cloud AI Platform Pipelines to Simplify Machine Learning Development
In a recent blog post, Google announced the beta of Cloud AI Platform Pipelines, which provides users with a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility.
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Oracle Cloud Now Offers Data Science and Machine Learning Services
Oracle recently announced the availability of its Cloud Data Science Platform, a native service on Oracle Cloud Infrastructure (OCI), which the software designed to let teams of data scientists collaborate on the development, deployment and maintenance of machine learning models.
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Algorithmia Adds GitHub Integration to Machine Learning Platform
Algorithmia, an AI model management automation platform for data scientists and machine learning (ML) engineers, now integrates with GitHub.