Auto-classification, ML and Software Development

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

ML

ML Machine Learning Auto-complete Auto-classification

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. We recently developed four more new models. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models.

ML

ML Deep Learning Python Auto-classification

Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. DynamoDB is used to store the pet attributes.

Auto-complete

Auto-complete Auto-classification Machine Learning ML

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Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

Auto-complete

Auto-complete Auto-classification ML Data Quality

Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning Blog

APRIL 8, 2024

For the TensorRT-LLM container, we use auto. option.tensor_parallel_degree=max option.max_rolling_batch_size=32 option.rolling_batch=auto option.model_loading_timeout = 7200 We package the serving.properties configuration file in the tar.gz Similarly, you can use log_prob as measure of confidence score for classification use cases.

Auto-complete

Auto-complete LLM Deep Learning Machine Learning

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

JULY 31, 2023

Although machine learning (ML) can provide valuable insights, ML experts were needed to build customer churn prediction models until the introduction of Amazon SageMaker Canvas. Cost-sensitive classification – In some applications, the cost of misclassification for different classes can be different.

Auto-classification

Auto-classification Machine Learning ML Auto-complete

Top Low-Code and No-Code Platforms for Data Science in 2023

ODSC - Open Data Science

APRIL 17, 2023

Finally, H2O AutoML has the ability to support a wide range of machine learning tasks such as regression, time-series forecasting, anomaly detection, and classification. Auto-ViML : Like PyCaret, Auto-ViML is an open-source machine learning library in Python. This makes Auto-ViML an ideal tool for beginners and experts alike.

Data Science

Data Science Auto-classification Machine Learning Data Scientist

Google Research, 2022 & Beyond: Language, Vision and Generative Models

Google Research AI blog

JANUARY 18, 2023

Language Models Computer Vision Multimodal Models Generative Models Responsible AI* Algorithms ML & Computer Systems Robotics Health General Science & Quantum Community Engagement * Other articles in the series will be linked as they are released. language models, image classification models, or speech recognition models).

Computer Vision

Computer Vision Auto-classification Large Language Models Neural Network

Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

AWS Machine Learning Blog

NOVEMBER 22, 2023

Operational excellence in IDP means applying the principles of robust software development and maintaining a high-quality customer experience to the field of document processing, while consistently meeting or surpassing service level agreements (SLAs). This post focuses on the Operational Excellence pillar of the IDP solution.

IDP

IDP Data Extraction Machine Learning ML

Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

AWS Machine Learning Blog

JUNE 12, 2023

It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more. Rahul Huilgol is a Senior Software Development Engineer in Distributed Deep Learning at Amazon Web Services.

Deep Learning

Deep Learning Auto-classification Computer Vision Machine Learning

Time series forecasting with Amazon SageMaker AutoML

AWS Machine Learning Blog

OCTOBER 8, 2024

In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). It provides a straightforward way to create high-quality models tailored to your specific problem type, be it classification, regression, or forecasting, among others.

Machine Learning

Machine Learning Auto-complete Auto-classification Metadata

Deploying Large NLP Models: Infrastructure Cost Optimization

The MLOps Blog

MARCH 23, 2023

These models have achieved various groundbreaking results in many NLP tasks like question-answering, summarization, language translation, classification, paraphrasing, et cetera. 5 Leverage serverless computing for a pay-per-use model, lower operational overhead, and auto-scaling. 2 Calculate the size of the model.

NLP

NLP LLM Large Language Models Natural Language Processing

Managing Computer Vision Projects with Micha? Tadeusiak

The MLOps Blog

FEBRUARY 27, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects. Then we are there to help.

Computer Vision

Computer Vision Auto-classification Auto-complete ML

Operationalizing knowledge for data-centric AI

Snorkel AI

FEBRUARY 27, 2023

His presentation also highlights the ways that Snorkel’s platform, Snorkel Flow, enables users to rapidly and programmatically label and develop datasets and then use them to train ML models. This is a platform that supports this new data-centric development loop. This could be something really simple.

Machine Learning

Machine Learning Large Language Models AI AI

Operationalizing knowledge for data-centric AI

Snorkel AI

FEBRUARY 27, 2023

His presentation also highlights the ways that Snorkel’s platform, Snorkel Flow, enables users to rapidly and programmatically label and develop datasets and then use them to train ML models. This is a platform that supports this new data-centric development loop. This could be something really simple.

Machine Learning

Machine Learning Large Language Models AI AI

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

JANUARY 17, 2024

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. As a publicly available model, Llama 2 is designed for many NLP tasks such as text classification, sentiment analysis, language translation, language modeling, text generation, and dialogue systems.

Auto-complete

Auto-complete Python Machine Learning Deep Learning

Deploy thousands of model ensembles with Amazon SageMaker multi-model endpoints on GPU to minimize your hosting costs

AWS Machine Learning Blog

AUGUST 8, 2023

In cases where the MME receives many invocation requests, and additional instances (or an auto-scaling policy) are in place, SageMaker routes some requests to other instances in the inference cluster to accommodate for the high traffic. Triton on SageMaker SageMaker enables model deployment using Triton server with custom code.

BERT

BERT Deep Learning Auto-classification Python

Announcing New Tools for Building with Generative AI on AWS

Flipboard

APRIL 13, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses.

Generative AI

Generative AI ML AI AI

Artificial Intelligence Zone

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

Webinars

Trending Sources

Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

Webinars

Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

Top Low-Code and No-Code Platforms for Data Science in 2023

Google Research, 2022 & Beyond: Language, Vision and Generative Models

Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

Time series forecasting with Amazon SageMaker AutoML

Deploying Large NLP Models: Infrastructure Cost Optimization

Managing Computer Vision Projects with Micha? Tadeusiak

Operationalizing knowledge for data-centric AI

Operationalizing knowledge for data-centric AI

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

Deploy thousands of model ensembles with Amazon SageMaker multi-model endpoints on GPU to minimize your hosting costs

Announcing New Tools for Building with Generative AI on AWS

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