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. path_suffix='.parquet',

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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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Hacker News

NOVEMBER 15, 2023

The broad potential is why companies including AWS , IBM , Glean , Google, Microsoft, NVIDIA, Oracle and Pinecone are adopting RAG. IBM’s Watson became a TV celebrity in 2011 when it handily beat two human champions on the Jeopardy! Today, LLMs are taking question-answering systems to a whole new level.

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AWS Machine Learning Blog

APRIL 19, 2023

Streaming ingestion – An Amazon Kinesis Data Analytics for Apache Flink application backed by Apache Kafka topics in Amazon Managed Streaming for Apache Kafka (MSK) (Amazon MSK) calculates aggregated features from a transaction stream, and an AWS Lambda function updates the online feature store.

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Console DevTools podcast

JULY 5, 2023

He went to Y Combinator in 2011 where he joined a company called MongoHQ (now Compose ) that hosted Mongo databases which he sold to IBM, before turning his attention to building Fly.io. I think you can on top of AWS now. Kurt sheds light on the regions where Fly.io Hosted by David Mytton ( Console ) and Jean Yang ( Akita Software ).

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Database AWS 52

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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AWS Machine Learning Blog

MAY 2, 2023

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). Managed Spot Training is supported in all AWS Regions where Amazon SageMaker is currently available.

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Algorithm Machine Learning Machine Learning Natural Language Processing 93

DagsHub

JULY 25, 2024

In 2011, H2O.ai Further Reading and Documentation Azure Machine Learning Documentation Azure Machine Learning Tutorials Amazon SageMaker Amazon SageMaker is a fully managed service from Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models quickly and at scale.

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Deep Learning Deep Learning Machine Learning Machine Learning 52

AWS Machine Learning Blog

JULY 20, 2023

All these solutions include a meta-estimator (for example in an AWS Lambda function) that invokes each model and implements the blending or voting function. Framework containers enable you to use ready-made environments managed by AWS that include all necessary configuration and modules. References [1] Raj Kumar, P. 34 (11): 1328–1341.

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Dataconomy

MARCH 18, 2025

billion in 2011. Google aims to bolster its position in the enterprise cloud market, where it has struggled against competitors like AWS and Microsoft Azure. Although both companies have yet to confirm the deal officially, multiple outlets are reporting its completion.

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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. The new Automated Reasoning checks safeguard is available today in preview in Amazon Bedrock Guardrails in the US West (Oregon) AWS Region. Happy building!

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AWS Machine Learning Blog

NOVEMBER 18, 2024

In this post, we show you how DXC and AWS collaborated to build an AI assistant using large language models (LLMs), enabling users to access and analyze different data types from a variety of data sources. His research has been published in top-tier conferences like NeurIPS, ICLR, AISTATS, and AAAI, as well as IEEE and ACM Transactions.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you’ll need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. He is specialized in architecting AI/ML and generative AI services at AWS.

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