Remove Data Platform Remove Information Remove ML Engineer
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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. These skilled professionals are tasked with building and deploying models that improve the quality and efficiency of BMW’s business processes and enable informed leadership decisions.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Thats why we use advanced technology and data analytics to streamline every step of the homeownership experience, from application to closing. Deployment times stretched for months and required a team of three system engineers and four ML engineers to keep everything running smoothly.

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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

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.

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

ODSC - Open Data Science

8 Tools to Protect Sensitive Data from Unintended Leakage In order to protect themselves from unintended leakage of sensitive information, organizations employ a variety of tools that scan repositories and code continuously to identify the secrets that are hard-coded within.

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Designing resilient cities at Arup using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

Data underpins Arup consultancy for clients with world-class collection and analysis providing insight to make an impact. SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data.

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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?

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The Future of Data-Centric AI Day 2: Snorkel Flow and Beyond

Snorkel AI

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.