Machine Learning

Machine learning engineering in 2026 spans two worlds: classical ML (scikit-learn, XGBoost, feature engineering) and deep learning (PyTorch, transformers, LLM fine-tuning). The gap between research and production has narrowed — tools like MLflow, DVC, and Ray Train make production ML accessible to any engineering team.

Classical ML vs Deep Learning vs LLMs

Core Stack

# Classical ML — scikit-learn + XGBoost
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier
from sklearn.model_selection import cross_val_score

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', XGBClassifier(n_estimators=500, learning_rate=0.05))
])
scores = cross_val_score(pipeline, X_train, y_train, cv=5, scoring='roc_auc')
print(f"AUC: {scores.mean():.3f} ± {scores.std():.3f}")

# Deep Learning — PyTorch 2.x
import torch
import torch.nn as nn
from torch.utils.data import DataLoader

model = nn.Sequential(
    nn.Linear(input_dim, 256),
    nn.ReLU(),
    nn.Dropout(0.3),
    nn.Linear(256, num_classes)
)

# torch.compile() — up to 2x speedup with one line
model = torch.compile(model)

optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.01)

Learning Path

1. ML fundamentals — supervised/unsupervised, bias-variance, cross-validation
2. Classical ML pipeline — feature engineering, scikit-learn, XGBoost, SHAP
3. PyTorch basics — tensors, autograd, training loop, GPU acceleration
4. Computer vision — CNNs, transfer learning with ResNet/EfficientNet
5. NLP with transformers — HuggingFace, fine-tuning BERT/RoBERTa
6. LLM fine-tuning — LoRA, QLoRA, dataset preparation, evaluation
7. MLOps — experiment tracking (MLflow), data versioning (DVC), serving (vLLM/BentoML)

Essential Libraries