AWS Machine Learning Blog

SEPTEMBER 4, 2024

SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring. The JSON document can be stored and versioned in an Amazon Simple Storage Service (Amazon S3) bucket. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models.

DevOps 101

DevOps ML Engineer Metadata ML 101

AWS Machine Learning Blog

JUNE 11, 2024

When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs and Neuron DLCs in the Neuron SDK release notes, with a link to the AWS documentation containing the DLAMI and DLC release notes. Starting with the AWS Neuron 2.18 In this post, we walk through some of the support highlights with Neuron 2.18.

Deep Learning 120

Deep Learning ML Automation Auto-complete 120

AWS Machine Learning Blog

JUNE 20, 2024

In addition, RAG architecture can lead to potential issues like retrieval collapse , where the retrieval component learns to retrieve the same documents regardless of the input. RAG evaluation concepts and metrics As mentioned previously, RAG-based generative AI application is composed of two main processes: retrieval and generation.

Auto-classification 130

Auto-classification LLM Prompt Engineer Prompt Engineering 130

Mlearning.ai

JULY 22, 2023

the UI for annotation, image ref: [link] The base containers that run when we put the CVAT stack up (not included auto annotation) (Semi) automated annotation The CVAT (semi) automated annotation allow user to use something call nuclio , which is a tool aimed to assist automated data science through serverless deployment.

Auto-complete 52

Auto-complete Computer Vision Automation Metadata 52

AWS Machine Learning Blog

OCTOBER 8, 2024

In the training phase, CSV data is uploaded to Amazon S3, followed by the creation of an AutoML job, model creation, and checking for job completion. All other columns in the dataset are optional and can be used to include additional time-series related information or metadata about each item.

Machine Learning 93

Machine Learning Auto-complete Auto-classification Metadata 93

AWS Machine Learning Blog

MAY 22, 2024

jpg and the complete metadata from styles/38642.json. You can follow the steps in the documentation to enable model access. Each product is identified by an ID such as 38642, and there is a map to all the products in styles.csv. From here, we can fetch the image for this product from images/38642.jpg

Generative AI 131

Generative AI Natural Language Processing Machine Learning Large Language Models 131

AWS Machine Learning Blog

NOVEMBER 14, 2024

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. It can take up to 20 minutes for the setup to complete.

ML 89

ML Auto-complete Auto-classification Machine Learning 89

AWS Machine Learning Blog

MAY 8, 2023

With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Input and output – These fields are required because NVIDIA Triton needs metadata about the model. The TensorRT_backend repo contains the documentation and source for the backend. script from the following cell.

ML 97

ML BERT Deep Learning Auto-complete 97

The MLOps Blog

JANUARY 19, 2024

<pre class =" hljs " style =" display : block; overflow-x: auto; padding: 0.5 <pre class =" hljs " style =" display : block; overflow-x: auto; padding: 0.5 Have a look at the Quickstart guide in Neptune’s documentation. Last, we’ll shuffle the dataset to ensure the model sees it in randomized order.

LLM 52

LLM Auto-complete Large Language Models Natural Language Processing 52

AWS Machine Learning Blog

MARCH 6, 2023

FSx for Lustre uses distributed file storage (stripping) and physically separates file metadata from file content to achieve high-performance read/writes. With the SageMaker distributed model parallel library, we documented a 175-billion parameter model training over 920 NVIDIA A100 GPUs.

Large Language Models 97

Large Language Models LLM Machine Learning Deep Learning 97

AWS Machine Learning Blog

SEPTEMBER 21, 2023

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab , build an active learning framework on AWS to automate the processing of passenger documents. “In We used Amazon Textract to automate information extraction from specific document fields such as name and passport number.

Auto-complete 122

Auto-complete Machine Learning Computer Vision ML 122

Snorkel AI

MARCH 2, 2023

Others, toward language completion and further downstream tasks. In terms of technology: generating code snippets, code translation, and automated documentation. In financial services: summary of financial documents, entity extraction. Very large core pie, and very efficient in certain sets of things.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

Others, toward language completion and further downstream tasks. In terms of technology: generating code snippets, code translation, and automated documentation. In financial services: summary of financial documents, entity extraction. Very large core pie, and very efficient in certain sets of things.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

AWS Machine Learning Blog

SEPTEMBER 24, 2024

Complete the following steps to set up your knowledge base: Sign in to your AWS account, then choose Launch Stack to deploy the CloudFormation template: Provide a stack name, for example contact-center-kb. When you’re done, the top level of your S3 bucket should contain six folders, each containing a single Word or PDF document.

Generative AI 97

Generative AI Auto-complete LLM Automation 97

The MLOps Blog

MARCH 12, 2024

Model management Teams typically manage their models, including versioning and metadata. It involves transforming textual data into numerical form, known as embeddings, representing the semantic meaning of words, sentences, or documents in a high-dimensional vector space. using techniques like RLHF.)

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models LLM 52

AWS Machine Learning Blog

JULY 16, 2024

It manages the availability and scalability of the Kubernetes control plane, and it provides compute node auto scaling and lifecycle management support to help you run highly available container applications. Consult the preceding documentation for installing the CLIs on other platforms accordingly.

Generative AI 126

Generative AI Auto-complete Auto-classification AI 126

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Training job resiliency with the job auto resume functionality – In this section, we demonstrate how scientists can submit and manage their distributed training jobs using either the native Kubernetes CLI (kubectl) or optionally the new HyperPod CLI (hyperpod) with automatic job recovery enabled.

Auto-complete 81

Auto-complete ML Machine Learning Automation 81