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LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

Unite.AI

It was in 2014 when ICML organized the first AutoML workshop that AutoML gained the attention of ML developers. ai, IBM Watson AI, Microsoft AzureML, and a lot more. The ML pipelines can be computed independently on the same datasets and then blended together using averaging (or weighted averaging).

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Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime.

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TinyML: Applications, Limitations, and It’s Use in IoT & Edge Devices

Unite.AI

In the past few years, Artificial Intelligence (AI) and Machine Learning (ML) have witnessed a meteoric rise in popularity and applications, not only in the industry but also in academia. It’s the major reason why its difficult to build a standard ML architecture for IoT networks.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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Accelerating sustainable modernization with Green IT Analyzer on AWS

IBM Journey to AI blog

Businesses are increasingly embracing data-intensive workloads, including high-performance computing, artificial intelligence (AI) and machine learning (ML). This situation triggered an auto-scaling rule set to activate at 80% CPU utilization. Due to the auto-scaling of the new EC2 instances, an additional t2.large

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Improved ML model deployment using Amazon SageMaker Inference Recommender

AWS Machine Learning Blog

Each machine learning (ML) system has a unique service level agreement (SLA) requirement with respect to latency, throughput, and cost metrics. We train an XGBoost model for a classification task on a credit card fraud dataset. We demonstrate how to set up Inference Recommender jobs for a credit card fraud detection use case.

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MedUnA: Efficient Medical Image Classification through Unsupervised Adaptation of Vision-Language Models

Marktechpost

Supervised learning in medical image classification faces challenges due to the scarcity of labeled data, as expert annotations are difficult to obtain. Researchers from Mohamed Bin Zayed University of AI and Inception Institute of AI propose MedUnA, a Medical Unsupervised Adaptation method for image classification.