Tag: ai/ml

Data preparation is a principal component of machine learning (ML) pipelines. In fact, it is estimated that data professionals spend about 80 percent of their time on data preparation. In this intensive competitive market, teams want to analyze data and extract more meaningful insights quickly. Customers are adopting more efficient and visual ways to build …

Read more “Prepare data from Amazon EMR for machine learning using Amazon SageMaker Data Wrangler”

Across all industries, machine learning (ML) models are getting deeper, workflows are getting more complex, and workloads are operating at larger scales. Significant effort and resources are put into making these models more accurate since this investment directly results in better products and experiences. On the other hand, making these models run efficiently in production …

Read more “Exafunction supports AWS Inferentia to unlock best price performance for machine learning inference”

In this post, we show how to train, deploy, and predict natural disaster damage with Amazon SageMaker with geospatial capabilities. We use the new SageMaker geospatial capabilities to generate new inference data to test the model. Many government and humanitarian organizations need quick and accurate situational awareness when a disaster strikes. Knowing the severity, cause, …

Read more “Damage assessment using Amazon SageMaker geospatial capabilities and custom SageMaker models”

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. Autopilot can also deploy trained models to real-time inference endpoints automatically. If you have workloads with spiky or unpredictable traffic patterns that can tolerate cold starts, then deploying …

Read more “Deploy Amazon SageMaker Autopilot models to serverless inference endpoints”

Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. With increasing focus on online privacy, there’s an opportunity to identify ML algorithms …

Read more “Private Ads Prediction with DP-SGD”

Posted by Malaya Jules, Program Manager, Google This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels. This year we are presenting over 50 …

Read more “Google at EMNLP 2022”

In previous blog post, we described an end-to-end identity verification solution in a single AWS Region. The solution uses the Amazon Rekognition APIs DetectFaces for face detection and CompareFaces for face comparison. We think of those APIs as stateless APIs because they don’t depend on an Amazon Rekognition face collection. They’re also idempotent, meaning repeated …

Read more “Improve scalability for Amazon Rekognition stateless APIs using multiple regions”

The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and iterate upon this model. One of the key ways to improve an ML model is by choosing better tunable parameters, known as hyperparameters. This is known as hyperparameter optimization (HPO). However, …

Read more “Use your own training scripts and automatically select the best model using hyperparameter optimization in Amazon SageMaker”

Editor’s note: This post is part of an ongoing series on IT predictions from Google Cloud experts. Check out the full list of our predictions on how IT will change in the coming years. Prediction: AI will be the primary driver for moving to a 4-day work week Enterprise use of artificial intelligence (AI) has …

Read more “IT prediction: AI could help realize the dream of the four-day work week”