Remove Cloud Data Remove Data Pipeline Remove Deep Learning Remove Machine Learning
article thumbnail

Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS

AWS Machine Learning Blog

To address the large value challenge, you can utilize the Amazon SageMaker distributed data parallelism feature (SMDDP). SageMaker is a fully managed machine learning (ML) service. With data parallelism, a large volume of data is split into batches. This reduces the development velocity and ability to fail fast.

AWS 90
article thumbnail

How Cloud Data Platforms improve Shopfloor Management

Data Science Blog

The fusion of data in a central platform enables smooth analysis to optimize processes and increase business efficiency in the world of Industry 4.0 using methods from business intelligence , process mining and data science. Cloud Data Platform for shopfloor management and data sources such like MES, ERP, PLM and machine data.

professionals

Sign Up for our Newsletter

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

article thumbnail

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

Flipboard

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

ML 95
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. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

ML 93
article thumbnail

The Cloud Connection: How Governance Supports Security

Alation

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.