article thumbnail

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

Flipboard

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

ML 153
article thumbnail

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

Data scientists search and pull features from the central feature store catalog, build models through experiments, and select the best model for promotion. Data scientists create and share new features into the central feature store catalog for reuse.

ML 121
professionals

Sign Up for our Newsletter

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

article thumbnail

Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

His mission is to enable customers achieve their business goals and create value with data and AI. He helps architect solutions across AI/ML applications, enterprise data platforms, data governance, and unified search in enterprises.

Metadata 149
article thumbnail

MakeBlobs + Fictional Synthetic Data, Adding Data to Domain-Specific LLMs, and What Tech Layoffs…

ODSC - Open Data Science

How to Add Domain-Specific Knowledge to an LLM Based on Your Data In this article, we will explore one of several strategies and techniques to infuse domain knowledge into LLMs, allowing them to perform at their best within specific professional contexts by adding chunks of documentation into an LLM as context when injecting the query.

article thumbnail

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

article thumbnail

Bring your own AI using Amazon SageMaker with Salesforce Data Cloud

AWS Machine Learning Blog

As a result, businesses can accelerate time to market while maintaining data integrity and security, and reduce the operational burden of moving data from one location to another. With Einstein Studio, a gateway to AI tools on the data platform, admins and data scientists can effortlessly create models with a few clicks or using code.

article thumbnail

How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

We’ll see how this architecture applies to different classes of ML systems, discuss MLOps and testing aspects, and look at some example implementations. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline? What is a feature store?