New hands-on tutorial on The Main Thread 🚀

I built a real-time emotion detector in Java using #Quarkus, #JavaCV, #WebSocketsNext, and #LangChain4j.

The pipeline streams webcam frames → detects faces locally → sends only the cropped face to a multimodal LLM (GPT-4o/Gemini) for emotion classification.

High-throughput + low latency = fun and surprisingly practical.

👉 https://www.the-main-thread.com/p/real-time-emotion-detection-quarkus-langchain4j-webcam

#JavaDevelopers #AIPipelines #MultimodalAI #FaceDetection

🤖 MLOps: The Missing Link in Your Machine Learning Strategy 🔗

MLOps bridges the gap between data science and engineering, creating sustainable ML systems that actually work in the real world.

A proper MLOps workflow includes:
🔄 Automated data ingestion
🧪 Continuous model training
📊 Performance monitoring
🚨 Drift detection
🚀 Seamless redeployment

👀 https://link.illustris.org/mlopscode2prod

#MachineLearning #MLOps #DataScience #AIEngineering #ModelDeployment #DataDrift #AIPipelines

MLOps Demystified: Deploying Your Machine Learning Models to Production – Seamlessly

📊 What is MLOps? The Complete Guide to Machine Learning Operations📊Master the complexities of MLOps with our comprehensive guide, we break down how MLOps b...

YouTube

Today Juan Luis Cano Rodríguez from QuantumBlack, AI by McKinsey will give a workshop at PyData Global titled "Who needs ChatGPT? Rock solid AI pipelines with Hugging Face and Kedro" in which attendees will learn how to create a complex AI pipeline using Hugging Face transformers and turn it into a Kedro project that cleanly separates code from configuration and data.

Tune in at 16:00 UTC! https://global2023.pydata.org/cfp/talk/NFZDPN/

#python #pydata #pydataglobal #pydataglobal2023 #kedro #huggingface #aipipelines

Who needs ChatGPT? Rock solid AI pipelines with Hugging Face and Kedro PyData Global 2023

In this tutorial you will learn how to create a complex AI pipeline using Hugging Face transformers, turn it into a Kedro project that cleanly separates code from configuration and data, and deploy it to production so it starts delivering value. To that end, we will build a system that summarizes and classifies social media posts using several Hugging Face pre-trained models. The outline will be as follows: 1. Introduction (5m) 2. Who needs ChatGPT? Commercial vs open-source AI (5m) 3. Fighting spaghetti data science with Kedro (15m) 4. Using Hugging Face models (15m) 5. Separating code from data using the Kedro catalog (10m) 6. Refactoring the code using Kedro pipelines (20m) 7. Deploying to production (15m) 8. Conclusions