MagicLex @TheMagicLex

Transforms caffeine and mild addictions into thoughts Works @ Hopsworks hopsworks.ai Stockholm, Sverige Joined March 2012

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Jim Dowling @jim_dowling

6 months ago

The top 10 fallacies of MLOps hopsworks.ai/post/the-10-fa… @Aurimas_Gr @paulabartabajo_ @iusztinpaul @moteropedrido

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Daniel D. Gutierrez @AMULETAnalytics

a year ago

Introducing The AI Lakehouse - hopsworks.ai/post/the-ai-la… #AI #LakeHouse @hopsworks

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Miguel Otero Pedrido @moteropedrido

a year ago

The 𝐅𝐓𝐈 (𝐟𝐞𝐚𝐭𝐮𝐫𝐞, 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠, 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞) architecture. A mental map to build ML Systems and not get lost in the process ... #MachineLearning #MLOps #DataScience

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Pau Labarta Bajo @paulabartabajo_

a year ago

Training ML models is easy. Transforming the data these models need is the hard part... until you learn this ↓↓↓ 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Building a real-world ML system is > 𝟭𝟬% about training and deploying ML models, and > 𝟵𝟬% about transforming the data these models…

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Jim Dowling @jim_dowling

a year ago

Why is building AI systems hard? Because even with the simplest feature that you can imagine, you need to consider where it will be computed based on whether it's a batch AI system or a real-time AI system. Luckily, there are some principles to guide you. A thread 🧵👇

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Hamel Husain @HamelHusain

a year ago

Just use Python

non aesthetic things @PicturesFoIder

a year ago

Just use Python

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Jim Dowling @jim_dowling

a year ago

From our SIGMOD'24 paper, an easier read on the work we have done and are doing on making #rondb the database for real-time AI applications. If you thought #redis is good enough for real-time AI, please read this and tell us if we have changed your mind. hopsworks.ai/post/rondb-a-r…

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Hopsworks @hopsworks

a year ago

Did you know that you can generate and manage your training data right from the Hopsworks UI? @TheMagicLex show how you can simplify your data workflow and enhance traceability with just a few clicks. 🔄 bit.ly/4eNRyyx