2011, AWS and ML - Data Science Current

Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets.

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Improving air quality with generative AI

AWS Machine Learning Blog

JUNE 18, 2024

On December 6 th -8 th 2023, the non-profit organization, Tech to the Rescue , in collaboration with AWS, organized the world’s largest Air Quality Hackathon – aimed at tackling one of the world’s most pressing health and environmental challenges, air pollution. As always, AWS welcomes your feedback.

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Amazon EC2 P5e instances are generally available

AWS Machine Learning Blog

SEPTEMBER 9, 2024

AWS is the first leading cloud provider to offer the H200 GPU in production. Additionally, network latency can become an issue for ML workloads on distributed systems, because data needs to be transferred between multiple machines. 48xlarge sizes through Amazon EC2 Capacity Blocks for ML.

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Webinars

How to Achieve High-Accuracy Results When Using LLMs

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Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning Blog

JULY 15, 2024

Faced with manual dubbing challenges and prohibitive costs, MagellanTV sought out AWS Premier Tier Partner Mission Cloud for an innovative solution. In the backend, AWS Step Functions orchestrates the preceding steps as a pipeline. Each step is run on AWS Lambda or AWS Batch. She received her Ph.D. After earning his Ph.D.

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Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

DECEMBER 14, 2023

Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform. The solution does not require porting the feature extraction code to use PySpark, as required when using AWS Glue as the ETL solution.

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From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

OCTOBER 23, 2023

Founded in 2011, Talent.com is one of the world’s largest sources of employment. The system is developed by a team of dedicated applied machine learning (ML) scientists, ML engineers, and subject matter experts in collaboration between AWS and Talent.com. The recommendation system has driven an 8.6%

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Predicting new and existing product sales in semiconductors using Amazon Forecast

AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We trained three models using data from 2011–2018 and predicted the sales values until 2021.

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Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

AWS Machine Learning Blog

MAY 2, 2023

JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19).

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Top 10 Deep Learning Platforms in 2024

DagsHub

JULY 25, 2024

This guarantees businesses can fully utilize deep learning in their AI and ML initiatives. You can make more informed judgments about your AI and ML initiatives if you know these platforms' features, applications, and use cases. In 2011, H2O.ai Further Reading and Documentation H2O.ai Documentation H2O.ai

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Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker

AWS Machine Learning Blog

JULY 20, 2023

As AI has evolved, we have seen different types of machine learning (ML) models emerge. Detailed deployment patterns for this kind of settings can be found in Model hosting patterns in Amazon SageMaker, Part 1: Common design patterns for building ML applications on Amazon SageMaker.

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Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads

AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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Build verifiable explainability into financial services workflows with Automated Reasoning checks for Amazon Bedrock Guardrails

AWS Machine Learning Blog

FEBRUARY 19, 2025

AWS FSI customers, including NASDAQ, State Bank of India, and Bridgewater, have used FMs to reimagine their business operations and deliver improved outcomes. Automated Reasoning cant predict future events or handle ambiguous situations, nor can it learn from new data such as ML models. To learn more, visit Amazon Bedrock Guardrails.

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Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

AWS Machine Learning Blog

NOVEMBER 11, 2024

Solution overview SageMaker JumpStart is a robust feature within the SageMaker machine learning (ML) environment, offering practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs). An AWS Identity and Access Management (IAM) role to access SageMaker. You can access the Meta Llama 3.2

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Data Science Current

Reinventing a cloud-native federated learning architecture on AWS

Improving air quality with generative AI

Webinars

Trending Sources

Amazon EC2 P5e instances are generally available

Webinars

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

Streamlining ETL data processing at Talent.com with Amazon SageMaker

From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker

Predicting new and existing product sales in semiconductors using Amazon Forecast

Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

Top 10 Deep Learning Platforms in 2024

Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker

Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads

Build verifiable explainability into financial services workflows with Automated Reasoning checks for Amazon Bedrock Guardrails

Fine-tune Meta Llama 3.2 text generation models for generative AI inference using Amazon SageMaker JumpStart

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