AWS Machine Learning Blog

APRIL 27, 2023

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. In Trainium, the GP-SIMD engines are tightly coupled with the on-chip caches using a high bandwidth streaming interface, which can sustain 2 TB/sec of memory bandwidth.

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Auto-complete Deep Learning Machine Learning ML 80

DataRobot Blog

JANUARY 10, 2023

DataRobot Notebooks is a fully hosted and managed notebooks platform with auto-scaling compute capabilities so you can focus more on the data science and less on low-level infrastructure management. Auto-scale compute. In the DataRobot left sidebar, there is a table of contents auto-generated from the hierarchy of Markdown cells.

Auto-classification 52

Auto-classification Auto-complete Data Scientist Data Science 52

AWS Machine Learning Blog

NOVEMBER 14, 2024

Optionally, if Account A and Account B are part of the same AWS Organizations, and the resource sharing is enabled within AWS Organizations, then the resource sharing invitation are auto accepted without any manual intervention. Following are the steps completed by using APIs to create and share a model package group across accounts.

ML 84

ML Auto-complete Auto-classification Machine Learning 84

AWS Machine Learning Blog

MARCH 1, 2023

These types of data are historical raw data from an ML perspective. For example, each log is written in the format of timestamp, user ID, and event information. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. ML engineers no longer need to manage this training metadata separately.

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Automation ETL Data Drift ML 96

AWS Machine Learning Blog

SEPTEMBER 18, 2023

ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code. In this post, we discuss the benefits of using Ray and Amazon SageMaker for distributed ML, and provide a step-by-step guide on how to use these frameworks to build and deploy a scalable ML workflow.

Machine Learning 118

Machine Learning ML Python Auto-complete 118

The MLOps Blog

JUNE 27, 2023

Can you see the complete model lineage with data/models/experiments used downstream? Can you debug system information? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. Is it fast and reliable enough for your workflow?

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Metadata Machine Learning Data Scientist Data Quality 134

The MLOps Blog

JANUARY 23, 2023

I believe the team will look something like this: Software delivery reliability: DevOps engineers and SREs ( DevOps vs SRE here ) ML-specific software: software engineers and data scientists Non-ML-specific software: software engineers Product: product people and subject matter experts Wait, where is the MLOps engineer?

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DevOps Metadata Software Engineer Data Scientist 59

The MLOps Blog

JULY 18, 2023

You need to have a structured definition around what you’re trying to do so your data annotators can label information for you. And even on the operation side of things, is there a separate operations team, and then you have your research or ml engineers doing these pipelines and stuff? Now, we’re not perfect.

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Conversational AI Natural Language Processing AI AI 52

AWS Machine Learning Blog

JULY 23, 2024

is an auto-regressive language model that uses an optimized transformer architecture. For more information about version updates, refer to Shut down and Update Studio Classic Apps. 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 You can find the Llama 3.1

Machine Learning 117

Machine Learning Computer Vision Python Auto-complete 117

The MLOps Blog

DECEMBER 29, 2022

People will auto-scale up to 10 GPUs to handle the traffic. Does it mean that the production code has to be rewritten by, for example, ML engineers manually to be optimized for GPU with each update? Each of them may be with separate resource constraints, auto-scaling policies, and such. That’ll be easier short term.

ML 52

ML Auto-complete Machine Learning Python 52