AWS Machine Learning Blog

JUNE 3, 2024

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Complete the following steps: Choose Prepare and analyze data. Complete the following steps: Choose Run Data quality and insights report. Choose Create. Choose Export.

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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. If your workloads can tolerate latency, consider deploying your model on Amazon SageMaker Asynchronous Inference with auto-scaling groups.

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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.

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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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Towards AI

APRIL 27, 2023

Nothing in the world motivates a team of ML engineers and scientists to spend the required amount of time in data annotation and labeling. Now if you see, it's a complete workflow optimization challenge centered around the ability to execute data-related operations 10x faster. It's a new need now.

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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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AWS Machine Learning Blog

OCTOBER 24, 2024

After the model has completed training, you will be routed to the Analyze tab. Note that your numbers might differ from the ones you see in the following figure, because of the stochastic nature of the ML process. You’ll see the following after the batch prediction is complete. Now the model is being created.

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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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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.

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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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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.

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The MLOps Blog

JUNE 27, 2023

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. MLOps workflows for computer vision and ML teams Use-case-centric annotations.

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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.

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TheSequence

MAY 24, 2023

data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ). Were there any research breakthroughs in StarCoder, or would you say it was more of a crafty ML engineering effort? In addition we labelled a PII dataset for code to train a PII detector.

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Heartbeat

SEPTEMBER 6, 2023

Comet Comet is a machine learning platform built to help data scientists and ML engineers track, compare, and optimize machine learning experiments. Image by Author If you want to end the experiment, you can use the end method of the Experiment object to mark the experiment as complete. #

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Pickl AI

JANUARY 23, 2023

Provides modularity as a series of completely configurable, independent modules that can be combined with the fewest restrictions possible. It is used by programmers to handle multidimensional arrays and gives users the option to optimize mathematical representations in ML applications. It is very fast and supports GPU.

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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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The MLOps Blog

JULY 18, 2023

But ideally, we strive for complete independence of the models in our system so that we can update them without then having to go update every other model in the pipeline – that’s a danger that you can run into. But it’s absolutely critical for most people in our space that you do some type of auto-scaling.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Technical Debt Mitigation : Amazon SageMaker , being a managed service, allowed us to free our ML engineers from the burden of inference, enabling them to focus more on our core platform features—this relief from technical debt is a significant advantage of using SageMaker, reassuring us of its efficiency.

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AWS Machine Learning Blog

DECEMBER 2, 2024

Challenges in deploying LLMs for inference As LLMs and their respective hosting containers continue to grow in size and complexity, AI and ML engineers face increasing challenges in deploying and scaling these models efficiently for inference. To run this benchmark, we use sub-minute metrics to detect the need for scaling.

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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.

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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. The complete code of the APP can be found here. It is built on top of OpenAI’s Generative Pretrained Transformer (GPT-3.5 If you liked the blog post pls.

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