ML Engineer
Od matematike do produkcijskog ML — roadmapa za ML inženjera.
6 koraka
1
1. Matematika
Linearna algebra, statistika, verovatnoća, kalkulus.
NumPy
Statistika
Linearna algebra
2
2. Python za ML
Pandas, NumPy, Matplotlib, Jupyter. Data manipulation.
Python
Pandas
NumPy
Matplotlib
3
3. ML Osnove
Supervised/unsupervised learning, scikit-learn, feature engineering.
scikit-learn
XGBoost
Feature Engineering
4
4. Deep Learning
Neural networks, PyTorch/TensorFlow, CNNs, RNNs, Transformers.
PyTorch
TensorFlow
Transformers
5
5. MLOps
Model training, deployment, monitoring, experiment tracking.
MLflow
Kubeflow
Weights & Biases
DVC
6
6. LLM & GenAI
Large Language Models, RAG, fine-tuning, prompt engineering.
LangChain
OpenAI API
HuggingFace
RAG
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