AWS Machine Learning Blog

JUNE 13, 2023

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. Quotas for SageMaker machine learning (ML) instances can vary between accounts. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. code_falcon40b_deepspeed/model.py

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AWS Machine Learning Blog

NOVEMBER 22, 2023

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. About the Authors Abhinandan Patni is a Senior Software Engineer at Amazon Search.

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AWS Machine Learning Blog

MAY 12, 2023

Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. In addition to creating notebooks, you can perform all the ML development steps to build, train, debug, track, deploy, and monitor your models in a single pane of glass in Studio.

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AWS Machine Learning Blog

MAY 31, 2023

The Amazon SageMaker Python SDK is an open-source library for training and deploying machine learning (ML) models on Amazon SageMaker. In such cases, data scientists have to provide these parameters to their ML model training and deployment code manually, by noting down subnets, security groups, and KMS keys. something: '1.0'

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AWS Machine Learning Blog

JUNE 11, 2024

This allows machine learning (ML) practitioners to rapidly launch an Amazon Elastic Compute Cloud (Amazon EC2) instance with a ready-to-use deep learning environment, without having to spend time manually installing and configuring the required packages. You also need the ML job scripts ready with a command to invoke them.

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ODSC - Open Data Science

MAY 25, 2023

Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!) sktime, the unified package for time series ML sktime supports many time series related learning tasks and objects! We encourage you to complete your user registration here: [link].

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Amazon Personalize allows you to add sophisticated personalization capabilities to your applications by using the same machine learning (ML) technology used on Amazon.com for over 20 years. No ML expertise is required. Complete creating the stack and monitor the status on the stack details page.

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AWS Machine Learning Blog

FEBRUARY 8, 2023

Machine learning (ML) has become ubiquitous. Our customers are employing ML in every aspect of their business, including the products and services they build, and for drawing insights about their customers. To build an ML-based application, you have to first build the ML model that serves your business requirement.

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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. In this post, we discuss deploying scalable machine learning (ML) models for diarizing media content using Amazon SageMaker , with a focus on the WhisperX model. This included incorporating auto scaling for scalability using SageMaker.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

With SageMaker HyperPod, machine learning (ML) practitioners can train FMs for weeks and months without disruption, and without having to deal with hardware failure issues. Auto-resume and healing capabilities One of the new features with SageMaker HyperPod is the ability to have auto-resume on your jobs. pretrain-model.sh

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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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. SageMaker allowed the Einstein team to use auto-scaling of these GPUs to meet demand without manual intervention.

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Flipboard

DECEMBER 13, 2024

Generative AI , AI, and machine learning (ML) are playing a vital role for capital markets firms to speed up revenue generation, deliver new products, mitigate risk, and innovate on behalf of their customers. About SageMaker JumpStart Amazon SageMaker JumpStart is an ML hub that can help you accelerate your ML journey.

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AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them.

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The MLOps Blog

JANUARY 23, 2023

Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies.

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Mlearning.ai

JUNE 22, 2023

In this article, we will delve into the three broad categories of transformer models based on their training methodologies: GPT-like (auto-regressive), BERT-like (auto-encoding), and BART/T5-like (sequence-to-sequence). In such cases, we might not always have a complete sequence we are mapping to/from.

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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. He holds a master’s degree in Computer Science & Software Engineering from the University of Syracuse.

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The MLOps Blog

FEBRUARY 27, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects. Then we are there to help.

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Snorkel AI

MARCH 2, 2023

I am Ali Arsanjani, and I lead partner engineering for Google Cloud, specializing in the area of AI-ML, and I’m very happy to be here today with everyone. Then we’re going to talk about adapting foundation models for the enterprise and how that affects the ML lifecycle, and what we need to potentially add to the lifecycle.

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Snorkel AI

MARCH 2, 2023

I am Ali Arsanjani, and I lead partner engineering for Google Cloud, specializing in the area of AI-ML, and I’m very happy to be here today with everyone. Then we’re going to talk about adapting foundation models for the enterprise and how that affects the ML lifecycle, and what we need to potentially add to the lifecycle.

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The MLOps Blog

JULY 18, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Jason Falks about deploying conversational AI products to production.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity.

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AWS Machine Learning Blog

MAY 2, 2023

Machine learning (ML) helps organizations generate revenue, reduce costs, mitigate risk, drive efficiencies, and improve quality by optimizing core business functions across multiple business units such as marketing, manufacturing, operations, sales, finance, and customer service. Set the target column as churn.

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AWS Machine Learning Blog

JANUARY 17, 2024

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. Then choose Train to start the training job on a SageMaker ML instance. For more details on how to get started and set up SageMaker Studio, refer to Amazon SageMaker Studio. We discuss both methods in this section.

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Unite.AI

MAY 28, 2024

MonsterGPT provides a chat interface with ability to understand instructions in natural language for launching, tracking and managing complete finetuning and deployment jobs. Designing and Implementing multi-node auto-scaling with high throughput serving engines such as vLLM for LLM deployments. This can be extremely expensive.

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AWS Machine Learning Blog

JULY 23, 2024

is an auto-regressive language model that uses an optimized transformer architecture. SageMaker Studio is a comprehensive integrated development environment (IDE) that offers a unified, web-based interface for performing all aspects of the machine learning (ML) development lifecycle. The Llama 3.1 At its core, Llama 3.1 Deploy Llama 3.1

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