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Here are the key resources from the article, along with practical implementations and commands:
1️⃣ Mathematics for Machine Learning
2️⃣ Awesome Data Science
3️⃣ Hands-On ML with Scikit-Learn, Keras & TensorFlow (Code Repo)
🔗 https://lnkd.in/ecbTp4AR
4️⃣ Best of ML Python
5️⃣ Awesome Machine Learning
6️⃣ Interpretable Machine Learning
7️⃣ Fast.ai – Deep Learning for Coders
8️⃣ Dive into Deep Learning
9️⃣ Made With ML
🔟 Awesome MLOps
1️⃣1️⃣ ML Interview Prep
1️⃣2️⃣ Git, Docker, Flask & FastAPI
🔗 https://realpython.com/ | https://lnkd.in/dpADd3gh
You Should Know:
Essential Linux & Python Commands for Data Science & MLOps
Git & Version Control
git clone https://github.com/username/repo.git git add . git commit -m "Initial commit" git push origin main
Docker for ML Deployment
docker build -t ml-model:latest . docker run -p 5000:5000 ml-model docker-compose up
Python Virtual Environment
python -m venv venv source venv/bin/activate Linux/Mac venv\Scripts\activate Windows pip install -r requirements.txt
FastAPI for Model Serving
from fastapi import FastAPI
app = FastAPI()
@app.get("/predict")
def predict(input_data: str):
return {"prediction": "result"}
Run with:
uvicorn app:app --reload
Jupyter Notebook for ML
jupyter notebook Shortcuts: Shift+Enter = Run cell Esc+A = Insert cell above Esc+B = Insert cell below
ML Model Training (Scikit-Learn Example)
from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier() model.fit(X_train, y_train) predictions = model.predict(X_test)
TensorFlow/Keras Deep Learning
import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy') model.fit(X_train, y_train, epochs=10)
MLOps Monitoring (Prometheus + Grafana)
docker run -d --name=prometheus -p 9090:9090 prom/prometheus docker run -d --name=grafana -p 3000:3000 grafana/grafana
What Undercode Say:
The future of MLOps and AI deployment will heavily rely on automation, Kubernetes, and edge computing. Expect more low-code AI tools and real-time model monitoring solutions.
Prediction:
By 2026, 75% of ML models will be deployed via serverless architectures, reducing infrastructure costs.
Expected Output:
A structured guide with actionable commands and MLOps workflows for deploying AI models efficiently. 🚀
IT/Security Reporter URL:
Reported By: Shirin Khosravi – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
