AWS Machine Learning Blog

SEPTEMBER 25, 2023

Large language models (LLMs) used to generate text sequences need immense amounts of computing power and have difficulty accessing the available high bandwidth memory (HBM) and compute capacity. Values include auto , scheduler , and lmi-dist. It improves throughput and doesn’t sacrifice the time to first byte latency.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

These models have revolutionized various computer vision (CV) and natural language processing (NLP) tasks, including image generation, translation, and question answering. To make sure that our endpoint can scale down to zero, we need to configure auto scaling on the asynchronous endpoint using Application Auto Scaling.

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AWS Machine Learning Blog

JULY 24, 2024

Einstein has a list of over 60 features, unlocked at different price points and segmented into four main categories: machine learning (ML), natural language processing (NLP), computer vision, and automatic speech recognition. These models are designed to provide advanced NLP capabilities for various business applications.

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AWS Machine Learning Blog

MAY 9, 2023

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.

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AWS Machine Learning Blog

MAY 25, 2023

Amazon Kendra is a highly accurate and intelligent search service that enables users to search unstructured and structured data using natural language processing (NLP) and advanced search algorithms. Prerequisites Complete the following prerequisite steps: If you’re a first-time user of QuickSight in your AWS account, sign up for QuickSight.

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Flipboard

MARCH 13, 2023

We orchestrate our ML training and deployment pipelines using Amazon Managed Workflows for Apache Airflow (Amazon MWAA), which enables us to focus more on programmatically authoring workflows and pipelines without having to worry about auto scaling or infrastructure maintenance. Sahil Thapar is an Enterprise Solutions Architect.

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Unite.AI

NOVEMBER 2, 2023

We focused our internal tech on computer vision to detect things in images and video (fires, accidents, logos, objects, etc.) and NLP to determine what people were talking about. Is the foundation of summaries, agent coaching, auto qualification, to name a few. to triangulate and validate when an “event” happens.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

To remove an element, omit the text parameter completely. A compact 5-cup single serve coffee maker in matt black with travel mug auto-dispensing feature. - Experienced in AI/ML, NLP, and Search, he is interested in building products that solves customer pain points with innovative technology. Parse and decode the response.

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AWS Machine Learning Blog

APRIL 8, 2024

This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion and token level log probabilities).

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AWS Machine Learning Blog

JUNE 13, 2023

In addition, all SageMaker real-time endpoints benefit from built-in capabilities to manage and monitor models, such as including shadow variants , auto scaling , and native integration with Amazon CloudWatch (for more information, refer to CloudWatch Metrics for Multi-Model Endpoint Deployments ). 2xlarge instances.

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AWS Machine Learning Blog

JUNE 13, 2023

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. His expertise lies in Deep Learning in the domains of Natural Language Processing (NLP) and Computer Vision.

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AWS Machine Learning Blog

MAY 31, 2023

Set up the environment To deploy a complete infrastructure including networking and a Studio domain, complete the following steps: Clone the GitHub repository. Provide a name for the stack (for example, networking-stack ), and complete the remaining steps to create the stack. something: '1.0'

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AWS Machine Learning Blog

MAY 31, 2024

It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. After deployment is complete, you will see that an endpoint is created. The output shows the expected JSON file content, illustrating the model’s natural language processing (NLP) and code generation capabilities.

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AWS Machine Learning Blog

JUNE 1, 2023

Prerequisites Before getting started, complete the following prerequisites: Create an AWS account or use an existing AWS account. Set up your resources After you complete all the prerequisites, you’re ready to deploy the solution. He is passionate about computer vision, NLP, Generative AI and MLOps.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

This time-consuming process must be completed before content can be dubbed into another language. SageMaker asynchronous endpoints support upload sizes up to 1 GB and incorporate auto scaling features that efficiently mitigate traffic spikes and save costs during off-peak times. Feel free to share your thoughts in the comments.

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AWS Machine Learning Blog

MAY 4, 2023

For ultra-large models that don’t fit into a single accelerator, data flows directly between accelerators with NeuronLink, bypassing the CPU completely. These endpoints are fully managed and support auto scaling. When the tracing is complete, the model is partitioned across the NeuronCores based on the tensor parallel degree.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. If you’re not actively using the endpoint for an extended period, you should set up an auto scaling policy to reduce your costs.

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Viso.ai

DECEMBER 22, 2023

The Segment Anything Model (SAM), a recent innovation by Meta’s FAIR (Fundamental AI Research) lab, represents a pivotal shift in computer vision. SAM performs segmentation, a computer vision task , to meticulously dissect visual data into meaningful segments, enabling precise analysis and innovations across industries.

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Viso.ai

DECEMBER 18, 2023

Organizations can easily source data to promote the development, deployment, and scaling of their computer vision applications. Viso Suite is the End-to-End, No-Code Computer Vision Platform – Learn more What is Synthetic Data? 1: Variational Auto-Encoder. Get a demo. Technique No.1:

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AWS Machine Learning Blog

MAY 30, 2023

Furthermore, the CPUUtilization metric shows a classic pattern of periodic high and low CPU demand, which makes this endpoint a good candidate for auto scaling. You can start with a smaller instance and scale out first as your compute demand changes. If all are successful, then the batch transform job is marked as complete.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Complete the following steps to edit an existing space: On the space details page, choose Stop space. Reconfigure the compute, storage, or runtime. To start using Amazon CodeWhisperer, make sure that the Resume Auto-Suggestions feature is activated. Choose Create JupyterLab space. For Name , enter a name for your Space.

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Viso.ai

APRIL 24, 2023

provides the world’s only end-to-end computer vision platform Viso Suite. The solution enables leading companies to build, deploy and scale real-world computer vision systems. The vision task of recognizing text from the cropped regions is called Scene Text Recognition (STR). Get a demo here.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

As a managed service with auto scaling, SageMaker makes parallel generation of multiple videos possible using either the same reference image with different reference videos or the reverse. Once the SageMaker HyperPod cluster deletion is complete, delete the CloudFormation stack. In his spare time, he loves running and hiking.

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AWS Machine Learning Blog

APRIL 16, 2024

To store information in Secrets Manager, complete the following steps: On the Secrets Manager console, choose Store a new secret. Complete the following steps: On the Secrets Manager console, choose Store a new secret. Always make sure that sensitive data is handled securely to avoid potential security risks.

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AWS Machine Learning Blog

MARCH 6, 2023

Although this post focuses on LLMs, most of its best practices are relevant for any kind of large-model training, including computer vision and multi-modal models, such as Stable Diffusion. The preparation of a natural language processing (NLP) dataset abounds with share-nothing parallelism opportunities.

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Google Research AI blog

FEBRUARY 28, 2023

Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M Re-contextualizing Fairness in NLP for India A dataset of region and religion-based societal stereotypes in India, with a list of identity terms and templates for reproducing the results from the " Re-contextualizing Fairness in NLP " paper.

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AWS Machine Learning Blog

APRIL 17, 2023

SageMaker LMI containers includes model download optimization by using the s5cmd library to speed up the model download time and container startup times, and eventually speed up auto scaling on SageMaker. A complete example that illustrates the no-code option can be found in the following notebook.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Llama 2 stands at the forefront of AI innovation, embodying an advanced auto-regressive language model developed on a sophisticated transformer foundation. The complete example is shown in the accompanying notebook. Its model parameters scale from an impressive 7 billion to a remarkable 70 billion.

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Explosion

NOVEMBER 21, 2019

from_disk("/path/to/s2v_reddit_2015_md") nlp.add_pipe(s2v) doc = nlp("A sentence about natural language processing.") from_disk("/path/to/s2v_reddit_2015_md") nlp.add_pipe(s2v) doc = nlp("A sentence about natural language processing.") import spacy from spacy.pipeline import EntityRuler nlp = spacy.blank("en") ruler = EntityRuler(nlp).from_disk("./fashion_brands_patterns.jsonl")

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The MLOps Blog

JANUARY 19, 2024

But nowadays, it is used for various tasks, ranging from language modeling to computer vision and generative AI. Related post Tokenization in NLP: Types, Challenges, Examples, Tools Read more For training, we’ll create a so-called prompt that contains not only the question and the context but also the answer.

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Mlearning.ai

MAY 23, 2023

Once the exploratory steps are completed, the cleansed data is subjected to various algorithms like predictive analysis, regression, text mining, recognition patterns, etc depending on the requirements. It is the discounting of those subjects that did not complete the trial. What are auto-encoders?

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AWS Machine Learning Blog

SEPTEMBER 21, 2023

In order to power these applications, as well as those using other data modalities like computer vision, we need a robust and efficient workflow to quickly annotate data, train and evaluate models, and iterate quickly. As part of this strategy, they developed an in-house passport analysis model to verify passenger IDs.

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

What is Llama 2 Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Write a response that appropriately completes the request.nn### Instruction:nWhen did Felix Luna die?nn### In this post, we walk through how to fine-tune Llama 2 pre-trained text generation models via SageMaker JumpStart.

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Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. Multiple methods exist for assigning importance scores to the inputs of an NLP model. A breakdown of this architecture is provided here. This is the first article in the series.

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AWS Machine Learning Blog

MAY 9, 2023

SageMaker MMEs can horizontally scale using an auto scaling policy and provision additional GPU compute instances based on specified metrics. Returns - pb_utils.ModelConfig An object containing the auto-completed model configuration """ def initialize(self, args): `initialize` is called only once when the model is being loaded.

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Flipboard

DECEMBER 2, 2024

With this feature, you can closely match your compute resource usage to your actual needs, potentially reducing costs during times of low demand. This enhancement builds upon the existing auto scaling capabilities in SageMaker, offering more granular control over resource allocation.

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AWS Machine Learning Blog

JANUARY 9, 2023

The models can be completely heterogenous, with their own independent serving stack. This includes loading the model from Amazon Simple Storage Service (Amazon S3), for example, database lookups to validate the input, obtaining pre-computed features from the feature store, and so on. ML inference options.

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