AWS Machine Learning Blog

MAY 22, 2024

jpg and the complete metadata from styles/38642.json. SageMaker starts and manages all of the necessary Amazon Elastic Compute Cloud (Amazon EC2) instances for us, supplies the appropriate Hugging Face container, uploads the specified scripts, and downloads data from our S3 bucket to the container to /opt/ml/input/data.

Generative AI 126

Generative AI Machine Learning Natural Language Processing Large Language Models 126

AWS Machine Learning Blog

MAY 31, 2023

Each model deployed with Triton requires a configuration file ( config.pbtxt ) that specifies model metadata, such as input and output tensors, model name, and platform. Set up your environment To set up your environment, complete the following steps: Launch a SageMaker notebook instance with a g5.xlarge xlarge instance.

ML 99

ML Auto-classification Auto-complete Natural Language Processing 99

AWS Machine Learning Blog

APRIL 16, 2024

To store information in Secrets Manager, complete the following steps: On the Secrets Manager console, choose Store a new secret. Complete the following steps: On the Secrets Manager console, choose Store a new secret. Always make sure that sensitive data is handled securely to avoid potential security risks.

Data Scientist 107

Data Scientist Generative AI Machine Learning ML 107

John Snow Labs

OCTOBER 12, 2023

In this release, we’ve focused on simplifying model sharing, making advanced features more accessible with FREE access to Zero-shot NER prompting, streamlining the annotation process with completions and predictions merging, and introducing Azure Blob backup integration. With the 5.4 substantially upgrades our annotation capabilities.

NLP 52

NLP Auto-complete Auto-classification Prompt Engineer 52

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

PyImageSearch

OCTOBER 2, 2023

Jump Right To The Downloads Section A Deep Dive into Variational Autoencoder with PyTorch Introduction Deep learning has achieved remarkable success in supervised tasks, especially in image recognition. in their paper Auto-Encoding Variational Bayes. Auto-Encoding Variational Bayes. Looking for the source code to this post?

Computer Vision 117

Computer Vision Deep Learning Neural Network Auto-complete 117

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. Note that the cell takes around 30 minutes to complete. !docker This is run as part of the generate_model.sh

ML 94

ML BERT Deep Learning Auto-complete 94

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. This results in faster restarts and workload completion. Amazon FSx is an open-source parallel file system, popular in high-performance computing (HPC).

Large Language Models 91

Large Language Models LLM Machine Learning ML 91

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 Here’s the complete code: import evaluate import torch from tqdm.auto import tqdm import numpy as np def get_logits_and_labels (sample_, max_new_tokens) : sample = sample_.copy()

LLM 52

LLM Auto-complete Large Language Models Natural Language Processing 52

The MLOps Blog

FEBRUARY 27, 2023

You would address it in a completely different way, depending on what’s the problem. This is more about picking, for some active learning or for knowing where the data comes from and knowing the metadata to focus on the data that are the most relevant to start with. This is a much smaller scale than Auto ML.

Computer Vision 52

Computer Vision Auto-classification Auto-complete ML 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. Training Now that our data preparation is complete, we’re ready to train our model with the created dataset.

Generative AI 120

Generative AI Auto-complete Auto-classification Deep Learning 120

AWS Machine Learning Blog

AUGUST 6, 2024

The following diagram shows how MusicGen, a single stage auto-regressive Transformer model, can generate high-quality music based on text descriptions or audio prompts. The generated music will be downloaded from the S3 bucket. MusicGen code is released under MIT, model weights are released under CC-BY-NC 4.0.

Auto-complete 88

Auto-complete Metadata Generative AI Deep Learning 88

AWS Machine Learning Blog

FEBRUARY 19, 2024

A SageMaker MME dynamically loads models from Amazon Simple Storage Service (Amazon S3) when invoked, instead of downloading all the models when the endpoint is first created. As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency.

ML 111

ML Auto-complete Deep Learning Machine Learning 111

AWS Machine Learning Blog

MAY 2, 2023

Complete the following steps to use Autopilot AutoML to build, train, deploy, and share an ML model with a business analyst: Download the dataset , upload it to an Amazon S3 ( Amazon Simple Storage Service ) bucket, and make a note of the S3 URI. Download the abalone dataset from Kaggle. Set the target column as churn.

ML 75

ML Data Scientist Auto-complete Metadata 75

Flipboard

NOVEMBER 27, 2024

The application needs to search through the catalog and show the metadata information related to all of the data assets that are relevant to the search context. The following diagram illustrates the end-to-end architecture, consisting of the metadata API layer, ingestion pipeline, embedding generation workflow, and frontend UI.

Metadata 149

Metadata Auto-complete Data Discovery ML Engineer 149

AWS Machine Learning Blog

FEBRUARY 3, 2025

They proceed to verify the accuracy of the generated answer by selecting the buttons, which auto play the source video starting at that timestamp. The knowledge base sync process handles chunking and embedding of the transcript, and storing embedding vectors and file metadata in an Amazon OpenSearch Serverless vector database.

UX Design 88

UX Design Auto-complete LLM Prompt Engineer 88