Scalable AI and Design Patterns by 2024

Author:2024
Language: eng
Format: epub

Chapter 7 SCalable aI for real-tIme and StreamIng data

Advanced Techniques for Scalable AI

1. Ensemble Learning

Ensemble learning involves combining predictions from multiple

models to enhance accuracy and robustness. This technique

is particularly useful for real-time applications where diverse

models contribute to the final decision.

Example:

In a fraud detection system, an ensemble of different machine

learning models can be employed to analyze transaction data.

The combined prediction provides a more reliable fraud detection

mechanism.

Code snippet (Python—scikit-learn):

```python

from sklearn.ensemble import VotingClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

from sklearn.linear_model import LogisticRegression

from sklearn.svm import SVC

from sklearn.ensemble import RandomForestClassifier

# Create individual models

model1 = LogisticRegression()

model2 = SVC()

model3 = RandomForestClassifier()

# Create an ensemble model

ensemble_model = VotingClassifier(estimators=[('lr', model1),

('svc', model2), ('rf', model3)], voting='hard')

# Train the ensemble model

ensemble_model.fit(X_train, y_train)

# Make predictions

predictions = ensemble_model.predict(X_test)

```

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Chapter 7 SCalable aI for real-tIme and StreamIng data

2. Federated Learning

Federated learning allows model training to occur across multiple

decentralized devices or servers without exchanging raw data.

This is particularly beneficial for real-time applications where

privacy is a concern.

Example:

In a healthcare scenario, where patient data is sensitive, federated

learning enables training a predictive model across various

hospitals without centralizing patient information.

Code snippet (Python—PySyft):

```python

import syft

import torch

# Create a PySyft hook

hook = syft.TorchHook(torch)

# Create virtual workers (simulating decentralized devices)

bob = syft.VirtualWorker(hook, id="bob")

alice = syft.VirtualWorker(hook, id="alice")

# Train a model using federated learning

model = torch.nn.Linear(2, 1)

optimizer = torch.optim.SGD(params=model.parameters(), lr=0.1)

for epoch in range(10):

# Send the model to the virtual workers

model = model.send(bob)

# Perform local training on each worker

bob_model = model.copy().send(bob)

alice_model = model.copy().send(alice)

bob_optimizer = torch.optim.SGD(params=bob_model.

parameters(), lr=0.1)

alice_optimizer = torch.optim.SGD(params=alice_model.

parameters(), lr=0.1)

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# Local training on each worker

for _ in range(5):

bob_optimizer.zero_grad()

bob_prediction = bob_model(X_bob)

bob_loss = loss(bob_prediction, y_bob)

bob_loss.backward()

bob_optimizer.step()

alice_optimizer.zero_grad

()

alice_prediction = alice_model(X_alice)

alice_loss = loss(alice_prediction, y_alice)

alice_loss.backward()

alice_optimizer.step()

# Aggregate model updates

with torch.no_grad():

model.weight.set_(((bob_model.weight.data + alice_model.

weight.data) / 2).get())

model.bias.set_(((bob_model.bias.data + alice_model.bias.

data) / 2).get())

# Get the model back from the virtual workers

model = model.get()

```

3. Neural Architecture Search (NAS)

NAS involves automating the process of designing neural network

architectures, leading to models optimized for specific tasks. This

technique is valuable for real-time applications where model

efficiency is crucial.

Example:

In a real-time speech recognition system, NAS can be employed

to automatically search for the most efficient neural network

architecture, minimizing computational requirements while

maintaining high accuracy.

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Code snippet (Python—Keras Tuner):

```python

from kerastuner.tuners import RandomSearch

from kerastuner.engine.hyperparameters import HyperParameters

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

# Define the model-building function for NAS

def build_model(hp):

model = Sequential()

model.add(Dense(units=hp.Int('units', min_value=32, max_

value=512, step=32), input_dim=8, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy',

metrics=['accuracy'])

return model

# Instantiate the RandomSearch tuner

tuner = RandomSearch(

build_model,

objective='val_accuracy',

max_trials=5,

directory='nas',

project_name='real_time_speech_recognition'

)

# Perform the search

tuner.search(x_train, y_train, epochs=5, validation_data=

(x_val, y_val))

```

109

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