AWS Machine Learning Blog

SEPTEMBER 18, 2024

With the SageMaker HyperPod auto-resume functionality, the service can dynamically swap out unhealthy nodes for spare ones to ensure the seamless continuation of the workload. Also included are SageMaker HyperPod cluster software packages, which support features such as cluster health check and auto-resume.

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The MLOps Blog

JUNE 27, 2023

Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. It provides a high-level API that makes it easy to define and execute data science workflows.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Amazon SageMaker is a fully managed service that allows developers and data scientists to quickly build, train, and deploy machine learning (ML) models. With SageMaker, you can deploy your ML models on hosted endpoints and get real-time inference results.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

This allows you to share the intended uses and assessed carbon impact of a model so that data scientists, ML engineers, and other teams can make informed decisions when choosing and running models. Unlike persistent endpoints, clusters are decommissioned when a batch transform job is complete.

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AWS Machine Learning Blog

APRIL 27, 2023

Take for example operators such as TopK, LayerNorm, or ZeroCompression, which read data from memory and only use it for a minimal number of ALU calculations. Regular CPU systems are completely memory bound for these calculations, and performance is limited by the time required to move the data into the CPU.

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AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. 2xlarge instances.

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AWS Machine Learning Blog

MARCH 1, 2023

The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately. We define another pipeline step, step_cond.

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DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. 42% of data scientists are solo practitioners or on teams of five or fewer people.

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AWS Machine Learning Blog

JULY 23, 2024

is an auto-regressive language model that uses an optimized transformer architecture. 405B-Instruct You can use Llama models for text completion for any piece of text. The Llama 3.1 At its core, Llama 3.1 Model Name Model ID Default instance type Supported instance types Meta-Llama-3.1-8B 24xlarge, ml.p5.48xlarge Meta-Llama-3.1-8B-Instruct

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Mlearning.ai

MAY 3, 2023

autogpt : Auto-GPT is an “Autonomous AI agent” that given a goal in natural language, will allow Large Language Models (LLMs) to think, plan, and execute actions for us autonomously. ML/AI Enthusiasts, and Learners Citizen Data Scientists who prefer a low code solution for quick testing. It uses OpenAI’s GPT-4 or GPT-3.5

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Heartbeat

SEPTEMBER 6, 2023

Data scientists need to track and compare the performance of different machine learning models while using different hyperparameters. When working on an ML project, we must compare various machine learning models with different hyperparameters and want to understand which models and hyperparameters are most effective for our use case.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Create a KMS key in the dev account and give access to the prod account Complete the following steps to create a KMS key in the dev account: On the AWS KMS console, choose Customer managed keys in the navigation pane. Choose Create key. For Key type , select Symmetric. For Script Path , enter Jenkinsfile. Choose Save.

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The MLOps Blog

JANUARY 23, 2023

Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. How about the ML engineer? Let me explain.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Data scientists have to address challenges like data partitioning, load balancing, fault tolerance, and scalability. ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code.

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TheSequence

MAY 24, 2023

I originally did a master's degree in physics focusing on astrophysics, but around that time, I noticed the breakthroughs happening in ML so I decided to switch the focus of my studies towards ML. data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ).

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The MLOps Blog

DECEMBER 29, 2022

People will auto-scale up to 10 GPUs to handle the traffic. Let’s assume that we have a model in production that is retrained manually by data scientists, potentially a change of network architecture, extra features, stuff like that every three months. Kyle: That’s a really good question.

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AWS Machine Learning Blog

DECEMBER 4, 2024

SageMaker AI makes sure that sensitive data stays completely within each customer’s SageMaker environment and will never be shared with a third party. It also empowers data scientists and ML engineers to do more with their models by collaborating seamlessly with their colleagues in data and analytics teams.

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AWS Machine Learning Blog

OCTOBER 24, 2024

It allows data scientists and machine learning engineers to interact with their data and models and to visualize and share their work with others with just a few clicks. SageMaker Canvas has also integrated with Data Wrangler , which helps with creating data flows and preparing and analyzing your data.

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