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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. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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

AWS Machine Learning Blog

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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

AWS Machine Learning Blog

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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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.

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How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

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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.

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Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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