AutoML 2025: Neural Architecture Search and Hyperparameter Optimization

The Rise of AutoML

Building high-performance deep learning models no longer requires weeks of manual experimentation. 2025's AutoML systems consistently find architectures that beat human-designed ones.

Neural Architecture Search (NAS)

The problem: Search the infinite space of network architectures for the best one.

Search Strategies

• Reinforcement Learning: Controller RNN proposes architectures, reward = validation accuracy
• Evolutionary: Population of architectures evolves via mutation/crossover
• Differentiable (DARTS): Continuous relaxation of architecture search
• One-shot (Once-for-All): Train one supernet, then extract subnetworks

Once-for-All (OFA) in 2025

The most practical NAS approach:

from ofa import OFANet

# Train once
ofa_network = OFANet(
    depth_choices=[2, 3, 4],
    width_choices=[4, 6, 8],
    kernel_size_choices=[3, 5, 7]
)
ofa_network.train(training_data)

# Sample many architectures without retraining
for depth in [2, 3, 4]:
    for width in [4, 6, 8]:
        subnet = ofa_network.sample_subnet(depth=depth, width=width)
        accuracy = subnet.evaluate(val_data)

Hyperparameter Optimization (HPO)

Algorithms for finding optimal hyperparameters:

• Bayesian Optimization: Gaussian processes model performance surface
• Hyperband: Early stopping of poor configurations
• BOHB: Bayesian + Hyperband combination

from hyperopt import fmin, tpe, hp

space = {
    'learning_rate': hp.loguniform('lr', -10, -3),
    'batch_size': hp.choice('bs', [16, 32, 64, 128]),
    'dropout': hp.uniform('dropout', 0, 0.5)
}

def objective(params):
    model = train_model(**params)
    return -model.val_accuracy

best = fmin(objective, space, algo=tpe.suggest, max_evals=100)

Tools of 2025

• Optuna: Most popular HPO framework
• Ray Tune: Distributed hyperparameter tuning
• AutoGluon: Amazon's end-to-end AutoML
• NNI (Neural Network Intelligence): Microsoft's toolkit

When to Use AutoML

• New domain: You don't know what works
• Limited expertise: Team without deep learning experts
• Many similar tasks: Tune once, deploy many times
• Competitive benchmarks: Squeeze out last 1-2% performance