Flipboard

AUGUST 17, 2023

Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster. In the processing job API, provide this path to the parameter of submit_jars to the node of the Spark cluster that the processing job creates. We attached the IAM role to the Redshift cluster that we created earlier.

ML 122

ML ML AWS Data Warehouse 122

O'Reilly Media

SEPTEMBER 19, 2023

A basic, production-ready cluster priced out to the low-six-figures. A company then needed to train up their ops team to manage the cluster, and their analysts to express their ideas in MapReduce. Plus there was all of the infrastructure to push data into the cluster in the first place. Goodbye, Hadoop. And it was good.

Hadoop 136

Hadoop Algorithm ML ML 136

AWS Machine Learning Blog

NOVEMBER 15, 2024

of persons present’ for the sustainability committee meeting held on 5th April, 2012? His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. WASHINGTON, D. 20036 1128 SIXTEENTH ST., WASHINGTON, D.

ML 112

ML ML Python AWS 112

AWS Machine Learning Blog

AUGUST 22, 2023

Usually, if the dataset or model is too large to be trained on a single instance, distributed training allows for multiple instances within a cluster to be used and distribute either data or model partitions across those instances during the training process. Each account or Region has its own training instances.

Machine Learning 98

Machine Learning Machine Learning AWS ML 98

AWS Machine Learning Blog

MAY 10, 2023

In addition to the IAM user and assumed role session scheduling the job, you also need to provide a role for the notebook job instance to assume for access to your data in Amazon Simple Storage Service (Amazon S3) or to connect to Amazon EMR clusters as needed.

AWS 102

AWS Data Scientist ML ML 102

AWS Machine Learning Blog

MARCH 10, 2025

Cost-efficiency and infrastructure optimization By moving away from GPU-based clusters to Fargate, our monthly infrastructure costs are now 78.47% lower, and our per-question costs have reduced by 87.6%. With a decade of experience at Amazon, having joined in 2012, Kshitiz has gained deep insights into the cloud computing landscape.

AWS 115

AWS Database AI AI 115

Mlearning.ai

APRIL 8, 2023

Use Cases : Sentiment Analysis, Machine Translation, Named Entity Recognition Significant papers: “ Learning word embeddings efficiently with noise-contrastive estimation ” by Mnih and Hinton (2012) “Sequence to sequence learning with neural machine translation” by Sutskever et al. 2020) “GPT-4 Technical report ” by Open AI.

Natural Language Processing 98

Natural Language Processing Algorithm Machine Learning Machine Learning 98

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. in 2012 is now widely referred to as ML’s “Cambrian Explosion.” As with leading LLMs, time series FMs are likely to be successfully trained on large clusters of PBAs. Work by Hinton et al.

AWS 117

AWS ML ML Clustering 117

AWS Machine Learning Blog

MARCH 15, 2023

Cluster resources are provisioned for the duration of your job, and cleaned up when a job is complete. The processing container image can either be a SageMaker built-in image or a custom image that you provide. The underlying infrastructure for a Processing job is fully managed by SageMaker.

AWS 145

AWS Machine Learning Machine Learning Data Scientist 145

Google Research AI blog

DECEMBER 8, 2022

A group of photons initially clustered on neighboring sites will evolve into a superposition of all possible paths each photon might have taken. Among all the possible configuration paths, the only possible scenario that survives is the configuration in which all photons remain clustered together in a bound state.

Clustering 52

Clustering AI AI 52

Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2012; Otsu, 1979; Long et al., Methodology In this study, we used the publicly available PASCAL VOC 2012 dataset (Everingham et al., References: Arbeláez, P.,

Deep Learning 52

Deep Learning Deep Learning Machine Learning Algorithm 52

phData

MARCH 22, 2024

c.i. { "Version": "2012-10-17", "Statement": [ { "Sid": "", "Effect": "Allow", "Principal": { "AWS": " " }, "Action": "sts:AssumeRole", "Condition": { "StringEquals": { "sts:ExternalId": " " } } } ] } Grant usage on external volume to the role used to create Iceberg tables. . impl org.apache.iceberg.aws.s3.S3FileIO

SQL 52

SQL AWS Database Data Lakes 52

Cambridge Intelligence

OCTOBER 19, 2023

” First release: 2012 Format: An open-source, multi-model (property graph, document and key-value) database with hosted and local options Top 3 advantages: Native multi-model – 3 database models in one, reducing costs of ownership and tech stack complexity.

Database 59

Database Azure Analytics Analytics 59

Explosion

FEBRUARY 18, 2015

The only problem is that the list really contains two clusters of words: one associated with the legal meaning of “pleaded”, and one for the more general sense. Sorting out these clusters is an area of active research. Hardware : Intel i7-3770 (2012) Efficiency is a major concern for NLP applications.

Clustering 52

Clustering Natural Language Processing Machine Learning Machine Learning 52

AWS Machine Learning Blog

APRIL 16, 2024

For Secret type , choose Credentials for Amazon Redshift cluster. Choose the Redshift cluster associated with the secrets. or later image versions. Complete the following steps: On the Secrets Manager console, choose Store a new secret. Enter the credentials used to log in to access Amazon Redshift as a data source.

SQL 130

SQL AWS Database Data Scientist 130

Explosion

FEBRUARY 18, 2015

The tutorial also recommends the use of Brown cluster features, and case normalization features, as these make the model more robust and domain independent. I use greedy decoding, not beam search; I use the arc-eager transition system; I use the Goldberg and Nivre (2012) dynamic oracle. spaCy’s tagger makes heavy use of these features.

Algorithm 40

Algorithm Python Clustering 40

Hacker News

JANUARY 9, 2024

And in 2012 we introduced Quantity to represent quantities with units in the Wolfram Language. but with things like clustering). Meanwhile, having realized that date arithmetic is involved in the “inner loop” of certain computations, we optimized it—achieving a more than 100x speedup in Version 14.0.

Python 181

Python Algorithm Machine Learning Machine Learning 181

Flipboard

MARCH 21, 2025

In the Redshift navigation pane, you can also see the datashare created between the source and the target cluster. In this case, because the data is shared in the same account but between different clusters, SageMaker Unified Studio creates a view in the target database and permissions are granted on the view.

SQL 133

SQL Data Analyst Data Warehouse AWS 133

DrivenData Labs

MAY 1, 2025

The final sub-models use broad semantic clustering, an ensemble of the provided acoustic features, a Whisper classification fine-tune, and a contrastive Whisper fine-tune, designed to focus the model on identifying features independent of age, gender, and semantic group. Cluster 0 was in English and included many people talking to an Alexa.

Decision Trees 147

Decision Trees Clustering Algorithm Machine Learning 147

Hacker News

MARCH 20, 2023

In 2012, with the permission of the police, Janette used a magnifying glass to find where several hairs came together in a cluster. As the death mask had been molded directly off the Somerton Man’s head, neck, and upper body, some of the man’s hair was embedded in the plaster of ­Paris—a potential DNA gold mine.

Database 182

Database Clustering AI AI 182

AWS Machine Learning Blog

OCTOBER 11, 2024

Amazon Bedrock Knowledge Bases provides industry-leading embeddings models to enable use cases such as semantic search, RAG, classification, and clustering, to name a few, and provides multilingual support as well.

Database 107

Database AWS Clustering Data Lakes 107