AWS Machine Learning Blog

DECEMBER 5, 2023

The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. Amazon Comprehend custom classification API is used to organize your documents into categories (classes) that you define. Custom classification is a two-step process.

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Marktechpost

SEPTEMBER 12, 2024

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.

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Marktechpost

FEBRUARY 22, 2025

Whether you’re working on product review classification, AI-driven recommendation systems, or domain-specific search engines, this method allows you to fine-tune large-scale models on a budget efficiently. Here is the Colab Notebook for the above project.

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Marktechpost

JULY 13, 2024

In an effort to track its advancement towards creating Artificial Intelligence (AI) that can surpass human performance, OpenAI has launched a new classification system. Level 5: Organizations The highest ranking level in OpenAI’s classification is Level 5, or “Organisations.”

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Marktechpost

JULY 12, 2024

The first generation, exemplified by CLIP and ALIGN, expanded on large-scale classification pretraining by utilizing web-scale data without requiring extensive human labeling. These models used caption embeddings obtained from language encoders to broaden the vocabulary for classification and retrieval tasks.

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Flipboard

JUNE 26, 2023

Overview of solution In this post, we go through the various steps to apply ML-based fuzzy matching to harmonize customer data across two different datasets for auto and property insurance. Run an AWS Glue ETL job to merge the raw property and auto insurance data into one dataset and catalog the merged dataset.

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ODSC - Open Data Science

MARCH 17, 2023

At the end of the day, why not use an AutoML package (Automated Machine Learning) or an Auto-Forecasting tool and let it do the job for you? After implementing our changes, the demand classification pipeline reduces the overall error in our forecasting process by approx. 21% compared to the Auto-Forecasting one — quite impressive!

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PyImageSearch

FEBRUARY 6, 2023

Table of Contents Training a Custom Image Classification Network for OAK-D Configuring Your Development Environment Having Problems Configuring Your Development Environment? Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g.,

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Marktechpost

NOVEMBER 25, 2023

Such a representation makes many subsequent tasks, including those involving vision, classification, recognition and segmentation, and generation, easier. Therefore, encoders, decoders, and auto-encoders can all be implemented using a roughly identical crate design. Furthermore, the crate model exhibits many useful features.

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Marktechpost

JULY 31, 2024

Modules include building neural networks with Keras, computer vision, natural language processing, audio classification, and customizing models with lower-level TensorFlow code. It covers various aspects, from using larger datasets to preventing overfitting and moving beyond binary classification.

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Marktechpost

DECEMBER 18, 2024

TabNine TabNine is an AI-powered code auto-completion tool developed by Codota, designed to enhance coding efficiency across a variety of Integrated Development Environments (IDEs). Designed primarily for image recognition and classification, its ideal for prototyping and educational purposes.

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Flipboard

NOVEMBER 21, 2023

Our objective is to demonstrate the combined power of MATLAB and Amazon SageMaker using this fault classification example. Here, you use Auto Features , which quickly extracts a broad set of time and frequency domain features from the dataset and ranks the top candidates for model training. classifierModel = fitctree(.

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Marktechpost

DECEMBER 16, 2023

The X-Raydar achieved a mean AUC of 0.919 on the auto-labeled set, 0.864 on the consensus set, and 0.842 on the MIMIC-CXR test. For testing, a consensus set of 1,427 images annotated by expert radiologists, an auto-labeled set (n=103,328), and an independent dataset, MIMIC-CXR (n=252,374), were employed.

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IBM Journey to AI blog

SEPTEMBER 5, 2024

Auto-constructed data lineage : Helps visualize the flow of data through systems without the need for complex hand-coded solutions. Auto-generated audit logs : Record data interactions to understand how employees use data. For example, a bank customer’s documents might have sensitive information, such as account numbers, hidden.

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Towards AI

JANUARY 28, 2024

One reason for rephrasing a regression problem into a classification problem could be that the user wants to focus on a specific price range and requires a model that can predict this range with high accuracy. Labeling The dataset contains continuous prices that are converted into categories with respect to the provided thresholds.

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AWS Machine Learning Blog

FEBRUARY 12, 2025

The brand might be willing to absorb the higher costs of using a more powerful and expensive FMs to achieve the highest-quality classifications, because misclassifications could lead to customer dissatisfaction and damage the brands reputation. Consider another use case of generating personalized product descriptions for an ecommerce site.

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Unite.AI

AUGUST 29, 2023

Furthermore, it was observed that when the developers used the interpolation augmentation method, there was a drop in model’s accuracy during quantization, but at the same time, there was also a boost in model’s inference speed, and classification generalization.

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Towards AI

FEBRUARY 13, 2023

Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more. The NLP tasks we’ll cover are text classification, named entity recognition, question answering, and text generation. The pipeline we’re going to talk about now is zero-hit classification.

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Becoming Human

MARCH 16, 2023

A typical application of GNN is node classification. The problems that GNNs are used to solve can be divided into the following categories: Node Classification: The goal of this task is to determine the labeling of samples (represented as nodes) by examining the labels of their immediate neighbors (i.e., their neighbors’ labels).

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AWS Machine Learning Blog

MARCH 15, 2024

Use case overview The use case outlined in this post is of heart disease data in different organizations, on which an ML model will run classification algorithms to predict heart disease in the patient. module.eks_blueprints_kubernetes_addons -auto-approve terraform destroy -target=module.m_fedml_edge_client_2.module.eks_blueprints_kubernetes_addons

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IBM Journey to AI blog

JULY 10, 2023

Existing sales and service engineers can use language-based generative AI to augment their skills and easily find contextual or industrial knowledge to help them deliver better customer experiences or solve problems faster.

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Flipboard

MARCH 24, 2025

Interactive Documentation: We showcased the power of FastAPIs auto-generated Swagger UI and ReDoc for exploring and testing APIs. This shared embedding space enables CLIP to perform tasks like zero-shot classification and cross-modal retrieval without additional fine-tuning.

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Marktechpost

MAY 28, 2024

CNNs excel in tasks like object classification, detection, and segmentation, achieving human-level accuracy in diagnosing conditions from radiographs, dermatology images, retinal scans, and more. Deep Learning in Medical Imaging: Deep learning, particularly through CNNs, has significantly advanced computer vision in medical imaging.

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ODSC - Open Data Science

MAY 25, 2023

Here’s what you need to know: sktime is a Python package for time series tasks like forecasting, classification, and transformations with a familiar and user-friendly scikit-learn-like API. Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!)

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Marktechpost

OCTOBER 8, 2023

By providing object instance-level classification and semantic labeling, 3D semantic instance segmentation tries to identify items in a given 3D scene represented by a point cloud or mesh. They use an auto-labeling approach to distinguish between known and unknowable class labels to produce pseudo-labels during training.

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AWS Machine Learning Blog

MAY 3, 2023

But from an ML standpoint, both can be construed as binary classification models, and therefore could share many common steps from an ML workflow perspective, including model tuning and training, evaluation, interpretability, deployment, and inference. The final outcome is an auto scaling, robust, and dynamically monitored solution.

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Pragnakalp

OCTOBER 6, 2024

Auto-Scaling for Dynamic Workloads One of the key benefits of using SageMaker for model deployment is its ability to auto-scale. pip install sagemaker pip install boto3 This Python code snippet demonstrates how to deploy a pre-trained DistilBERT model from Hugging Face onto AWS SageMaker for text classification tasks.

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Marktechpost

SEPTEMBER 13, 2024

In supervised image classification and self-supervised learning, there’s a trend towards using richer pointwise Bernoulli conditionals parameterized by sigmoid functions, moving away from output conditional categorical distributions typically parameterized by softmax.

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Marktechpost

AUGUST 1, 2023

” This generated text is stored as metadata, enabling more efficient video classification and facilitating search engine accessibility. Flamingo employs its advanced visual language model to generate explanatory text by analyzing the initial frames of YouTube Shorts videos.

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AWS Machine Learning Blog

MAY 2, 2023

With the ability to solve various problems such as classification and regression, XGBoost has become a popular option that also falls into the category of tree-based models. These models have long been used for solving problems such as classification or regression. threshold – This is a score threshold for determining classification.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

To frame this research and give concrete evaluation targets, Thomson Reuters focused on several real-world tasks: legal summarization, classification, and question answering. Legal classification In other legal tasks, such as classification that was measured in accuracy and precision or recall, there’s still room to improve.

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Towards AI

MAY 12, 2024

A CNN is a neural network with one or more convolutional layers and is used mainly for image processing, classification, segmentation, and other auto-correlated data. Yann LeCun et al.,

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Marktechpost

AUGUST 10, 2023

By leveraging pre-trained LLMs and powerful vision foundation models (VFMs), the model demonstrates promising performance in discriminative tasks like image-text retrieval and zero classification, as well as generative tasks such as visual question answering (VQA), visual reasoning, image captioning, region captioning/VQA, etc.

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Google Research AI blog

FEBRUARY 10, 2023

Relative performance results of three GNN variants ( GCN , APPNP , FiLM ) across 50,000 distinct node classification datasets in GraphWorld. Structure of auto-bidding online ads system. We find that academic GNN benchmark datasets exist in regions where model rankings do not change.

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The MLOps Blog

JANUARY 23, 2025

An output could be, e.g., a text, a classification (like “dog” for an image), or an image. It can perform visual dialogue, visual explanation, visual question answering, image captioning, math equations, OCR, and zero-shot image classification with and without descriptions. Basic structure of a multimodal LLM.

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Snorkel AI

MARCH 30, 2023

Here’s an overview of the Data-centric Foundation Model Development capabilities: Warm Start: Auto-label training data using the power of FMs + state-of-the-art zero- or few-shot learning techniques during onboarding, helping get to a powerful baseline “first pass” with minimal human effort.

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Unite.AI

DECEMBER 1, 2023

Researchers still do great work in model-centric AI, but off-the-shelf models and auto ML techniques have improved so much that model choice has become commoditized at production time. When that’s the case, the best way to improve these models is to supply them with more and better data.

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