article thumbnail

Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning Blog

This post details how Purina used Amazon Rekognition Custom Labels , AWS Step Functions , and other AWS Services to create an ML model that detects the pet breed from an uploaded image and then uses the prediction to auto-populate the pet attributes. Start the model version when training is complete.

article thumbnail

Managing Computer Vision Projects with Micha? Tadeusiak 

The MLOps Blog

You would address it in a completely different way, depending on what’s the problem. 2 The more interesting ones are the ones that don’t have the data science teams, or sometimes they don’t even have software developers in the way that they are companies that live in the 21st century.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning Blog

This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion and token level log probabilities).

article thumbnail

Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

Another option is to download complete data for your ML model training use cases using SageMaker Data Wrangler processing jobs. After you check out the data type matching applied by SageMaker Data Wrangler, complete the following steps: Choose the plus sign next to Data types and choose Add analysis. This is a one-time setup.

article thumbnail

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab. Cost-sensitive classification – In some applications, the cost of misclassification for different classes can be different.

article thumbnail

Google Research, 2022 & Beyond: Language, Vision and Generative Models

Google Research AI blog

We have also seen significant success in using large language models (LLMs) trained on source code (instead of natural language text data) that can assist our internal developers, as described in ML-Enhanced Code Completion Improves Developer Productivity. The pixels in the same colors are attended together.

article thumbnail

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. As a publicly available model, Llama 2 is designed for many NLP tasks such as text classification, sentiment analysis, language translation, language modeling, text generation, and dialogue systems. instance_type="ml.trn1n.32xlarge",