Learned
🔹 Deploying ML models to Kubernetes with kind
🔹 Pods, Deployments & Services
🔹 Writing YAML configs
🔹 Horizontal Pod Autoscaler (HPA)
✨ Watched ML service auto-scale from 1→3 replicas under load
github.com/HighviewOne/machine-learning-zoomcamp-homework/tree/main/HW10
Learned
🔹 Deploying ML models to Kubernetes with kind
🔹 Pods, Deployments & Services
🔹 Writing YAML configs
🔹 Horizontal Pod Autoscaler (HPA)
✨ Watched ML service auto-scale from 1→3 replicas under load
github.com/HighviewOne/machine-learning-zoomcamp-homework/tree/main/HW10
Learned
🔹 Serverless deployment w AWS Lambda
🔹 ONNX model conversion & inference
🔹 Docker containers for Lambda
🔹 Building & testing locally before deploying
✨ Takeaway: Preprocessing must match training — wrong normalization = wrong predictions
➡️ Module 10 Kubernetes
Learned
🔹 Serverless deployment w AWS Lambda
🔹 ONNX model conversion & inference
🔹 Docker containers for Lambda
🔹 Building & testing locally before deploying
✨ Takeaway: Preprocessing must match training — wrong normalization = wrong predictions
➡️ Module 10 Kubernetes
✨ React + Express + WebSockets
🎨 Monaco Editor syntax highlighting
⚡ Browser code execution (WASM)
✅ 8 pass tests
🌐 Deployed>Railway
02-coding-interview-production.up.railway.app/
github.com/HighviewOne/02-coding-interview
github.com/DataTalksClub/ai-dev-tools-zoomcamp/
✨ React + Express + WebSockets
🎨 Monaco Editor syntax highlighting
⚡ Browser code execution (WASM)
✅ 8 pass tests
🌐 Deployed>Railway
02-coding-interview-production.up.railway.app/
github.com/HighviewOne/02-coding-interview
github.com/DataTalksClub/ai-dev-tools-zoomcamp/
🔹 Building CNNs from scratch w/ PyTorch
🔹 BCEWithLogitsLoss for binary classification
🔹 Data augmentation (rotation, crop, flip)
🔹 Model architecture: 20M+ parameters!
✨ Takeaway: Data augmentation boosted validation accuracy from 71% → 75%. Small changes, big impact!
🔹 Building CNNs from scratch w/ PyTorch
🔹 BCEWithLogitsLoss for binary classification
🔹 Data augmentation (rotation, crop, flip)
🔹 Model architecture: 20M+ parameters!
✨ Takeaway: Data augmentation boosted validation accuracy from 71% → 75%. Small changes, big impact!
Zero Django knowledge → working app ✨
Repo: github.com/HighviewOne/...
AI-assisted coding is incredible 🚀
#AIDevTools #Django #LearningInPublic
Course by DataTalks.Club
github.com/DataTalksClub/ai-dev-tools-zoomcamp
Zero Django knowledge → working app ✨
Repo: github.com/HighviewOne/...
AI-assisted coding is incredible 🚀
#AIDevTools #Django #LearningInPublic
Course by DataTalks.Club
github.com/DataTalksClub/ai-dev-tools-zoomcamp
wine quality prediction system:
- random forest achieving 88% accuracy
- flask REST API
- dockerized deployment
- trained on 1,599 wine samples
alcohol content matters most for quality 📈
github.com/HighviewOne/...
@mlzoomcamp
#MachineLearning #Python #Docker
wine quality prediction system:
- random forest achieving 88% accuracy
- flask REST API
- dockerized deployment
- trained on 1,599 wine samples
alcohol content matters most for quality 📈
github.com/HighviewOne/...
@mlzoomcamp
#MachineLearning #Python #Docker
Tell them:
1. Vote NO on Cloture
AND
2. Vote NO on the Republican spending bill.
Don’t let them pivot to reconciliation. GOP doesn’t need Dem votes on that and they know it.
TODAY is the showdown.
🤳🏽: (202) 224-3121
Tell them:
1. Vote NO on Cloture
AND
2. Vote NO on the Republican spending bill.
Don’t let them pivot to reconciliation. GOP doesn’t need Dem votes on that and they know it.
TODAY is the showdown.
🤳🏽: (202) 224-3121