AWS Machine Learning Blog

APRIL 7, 2023

As an example, smart venue solutions can use near-real-time computer vision for crowd analytics over 5G networks, all while minimizing investment in on-premises hardware networking equipment. To learn more about deploying geo-distributed applications on AWS Wavelength, refer to Deploy geo-distributed Amazon EKS clusters on AWS Wavelength.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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AWS Machine Learning Blog

MARCH 10, 2025

Amazon Bedrock Guardrails implements content filtering and safety checks as part of the query processing pipeline. Anthropic Claude LLM performs the natural language processing, generating responses that are then returned to the web application.

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Heartbeat

DECEMBER 5, 2023

Third-generation Tensor Cores have accelerated AI tasks, leading to breakthroughs in image recognition, natural language processing, and speech recognition. Below, 8 different A100 hardware configurations are compared for the same Natural Language Processing (NLP) inference. How Many Are Needed?

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Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.

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Pickl AI

NOVEMBER 13, 2024

Typically used for clustering (grouping data into categories) or dimensionality reduction (simplifying data without losing important information). Resource Allocation : Optimising the allocation of resources in industries such as telecommunications or cloud computing. Often used for exploratory Data Analysis.

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AWS Machine Learning Blog

AUGUST 14, 2023

In this post and accompanying notebook, we demonstrate how to deploy the BloomZ 176B foundation model using the SageMaker Python simplified SDK in Amazon SageMaker JumpStart as an endpoint and use it for various natural language processing (NLP) tasks. Question: When was NLP Cloud founded?

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Pickl AI

NOVEMBER 28, 2024

Familiarity with cloud computing tools supports scalable model deployment. Key techniques in unsupervised learning include: Clustering (K-means) K-means is a clustering algorithm that groups data points into clusters based on their similarities. It’s often used in customer segmentation and anomaly detection.

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AWS Machine Learning Blog

OCTOBER 30, 2024

These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning.

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The MLOps Blog

JUNE 27, 2023

For example, if your team works on recommender systems or natural language processing applications, you may want an MLOps tool that has built-in algorithms or templates for these use cases. The vendor offerings are divided into two classes: GPU Cloud Servers are long-running (but possibly pre-emptible) machines.

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Pickl AI

SEPTEMBER 23, 2024

AI is making a difference in key areas, including automation, language processing, and robotics. Natural Language Processing: NLP helps machines understand and generate human language, enabling technologies like chatbots and translation.

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DrivenData Labs

DECEMBER 11, 2024

A number of breakthroughs are enabling this progress, and here are a few key ones: Compute and storage - The increased availability of cloud compute and storage has made it easier and cheaper to get the compute resources organizations need.

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AWS Machine Learning Blog

DECEMBER 18, 2024

During the training process, our SageMaker HyperPod cluster was connected to this S3 bucket, enabling effortless retrieval of the dataset elements as needed. The deduplication process involved embedding dataset elements using a text embedder, then computing cosine similarity between the embeddings to identify similar elements.

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