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I build real-world ML products. And then help you do the same 🚀
Wanna learn how to do 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 in 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 ⚡ using 𝗞𝗮𝗳𝗸𝗮, 𝗗𝗼𝗰𝗸𝗲𝗿 and 𝗣𝘆𝘁𝗵𝗼𝗻 🐍 ? ↓↓↓ 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 🏔️ Feature engineering is about transforming raw data into predictive signals for your Machine Learning model. And for many real-word problems, you need to run this transformation in 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲. Otherwise, your features will be irrelevant. So, as a professional ML engineer, you need to -> go beyond static CSV files and local Jupyter notebooks, and -> learn the tools that help you build production-ready real-time systems ⚡. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 💁 In this video I show you step-by-step how to build a real-time feautre-engineering service that - 𝗿𝗲𝗮𝗱𝘀 raw crypto-trade data from a Kafka topic (input topic), - 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝘀 this raw data into Open-High-Low-Close features using stateful window aggregations, and - 𝘀𝗮𝘃𝗲𝘀 the final features into another Kafka topic (output topic), so the data is accessible to downstream services. 𝗧𝗼𝗼𝗹𝘀 🛠️ We will use are > 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗼𝗲𝘁𝗿𝘆 to package our Python code professionally > Quix Streams to do window aggregations in real-time ⚡ > Docker, Inc and Docker Compose to containerize our service. > Redpanda Data to enable communication between our service and the rest of the infrastructure. ---- Hi there! It's Pau Labarta Bajo 👋 Every day I share free, hands-on content, on production-grade ML, to help you build real-world ML products. 𝗙𝗼𝗹���𝗼𝘄 𝗺𝗲 and 𝗰𝗹𝗶𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 🔔 so you don't miss what's coming next #machinelearning #realtimedata #mlops #realworldml
Sachin Kumar
2w
Thanks for sharing Pau Labarta Bajo
Very informative. Thank you 💡
Useful tips
Interesting!
Great 👍
I build real-world ML products. And then help you do the same 🚀
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