Remove Cloud Data Remove Data Preparation Remove Data Science Remove Deep Learning
article thumbnail

Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

The data science team expected an AI-based automated image annotation workflow to speed up a time-consuming labeling process. Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units.

AWS 87
article thumbnail

Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

Flipboard

Data scientists and ML engineers require capable tooling and sufficient compute for their work. Therefore, BMW established a centralized ML/deep learning infrastructure on premises several years ago and continuously upgraded it. More importantly, the use of these platforms was misaligned with BMW Group’s IT cloud-first strategy.

ML 95
professionals

Sign Up for our Newsletter

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

article thumbnail

Introducing watsonx: The future of AI for business

IBM Journey to AI blog

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring.

AI 107
article thumbnail

Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. If you are prompted to choose a kernel, choose Data Science as the image and Python 3 as the kernel, then choose Select.

ML 93