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You still don’t need a feature store

Xebia

This becomes more important when a company scales and runs more machine learning models in production. Please have a look at this blog post on machine learning serving architectures if you do not know the difference. Let’s say you are a Data Scientist working in a model development environment.

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Make the leap to Hybrid with Cloudera Data Engineering

Cloudera

When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale. Each unlocking value in the data engineering workflows enterprises can start taking advantage of. Usage Patterns.

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Building a vision for real-time artificial intelligence

CIO

Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence. Real-time AI involves processing data for making decisions within a given time frame. It isn’t easy.

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Building Custom Runtimes with Editors in Cloudera Machine Learning

Cloudera

Cloudera Machine Learning (CML) is a cloud-native and hybrid-friendly machine learning platform. It unifies self-service data science and data engineering in a single, portable service as part of an enterprise data cloud for multi-function analytics on data anywhere. Click +Add Runtime.

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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

AWS Machine Learning - AI

Cost and Performance The solution achieves remarkable throughput by processing 100,000 documents within a 12-hour window. Serverless on AWS AWS GovCloud (US) Generative AI on AWS About the Authors Nick Biso is a Machine Learning Engineer at AWS Professional Services. He is also the #1 Square Off player in the world.

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Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

Netflix Tech

Since memory management is not something one usually associates with classification problems, this blog focuses on formulating the problem as an ML problem and the data engineering that goes along with it. Some nuances while creating this dataset come from the on-field domain knowledge of our engineers.

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V7 snaps up $33M to automate training data for computer vision AI models

TechCrunch

Radical Ventures and Temasek are co-leading this round, w1ith Air Street Capital, Amadeus Capital Partners and Partech (three previous backers ) also participating, along with a number of individuals prominent in the world of machine learning and AI. Image Credits: V7 Labs (opens in a new window).

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