💎 Machine Learning with TuskLang and Ruby
Machine Learning with TuskLang and Ruby
🤖 Intelligence at Your Fingertips
TuskLang enables sophisticated machine learning capabilities for Ruby applications, providing model training, prediction, feature engineering, and ML pipeline management. Build intelligent systems that learn, adapt, and make predictions with precision.
🚀 Quick Start: ML Pipeline
Basic Machine Learning Configuration
config/machine_learning.tsk
[machine_learning]
enabled: @env("ML_ENABLED", "true")
framework: @env("ML_FRAMEWORK", "tensorflow") # tensorflow, pytorch, scikit-learn
gpu_enabled: @env("ML_GPU_ENABLED", "false")
parallel_training: @env("ML_PARALLEL_TRAINING", "true")
model_storage: @env("ML_MODEL_STORAGE", "local") # local, s3, gcs[training]
enabled: @env("ML_TRAINING_ENABLED", "true")
batch_size: @env("ML_BATCH_SIZE", "32")
epochs: @env("ML_EPOCHS", "100")
validation_split: @env("ML_VALIDATION_SPLIT", "0.2")
early_stopping: @env("ML_EARLY_STOPPING", "true")
[prediction]
enabled: @env("ML_PREDICTION_ENABLED", "true")
batch_prediction: @env("ML_BATCH_PREDICTION", "true")
real_time_prediction: @env("ML_REAL_TIME_PREDICTION", "true")
prediction_cache: @env("ML_PREDICTION_CACHE", "true")
ML Pipeline Implementation
lib/ml_pipeline.rb
require 'tusk'
require 'redis'
require 'json'
require 'securerandom'
require 'concurrent'class MLPipeline
def initialize(config_path = 'config/machine_learning.tsk')
@config = Tusk.load(config_path)
@redis = Redis.new(url: @config['redis']['url'])
@models = {}
@pipelines = {}
@feature_engines = {}
setup_ml_pipeline
end
def create_pipeline(pipeline_name, pipeline_config)
return { success: false, error: 'ML disabled' } unless @config['machine_learning']['enabled'] == 'true'
pipeline_id = SecureRandom.uuid
pipeline = {
id: pipeline_id,
name: pipeline_name,
config: pipeline_config,
status: 'created',
created_at: Time.now.iso8601,
updated_at: Time.now.iso8601
}
@pipelines[pipeline_name] = pipeline
@redis.hset('ml_pipelines', pipeline_name, pipeline.to_json)
{
success: true,
pipeline_id: pipeline_id,
pipeline_name: pipeline_name
}
end
def train_model(pipeline_name, training_data, model_config = {})
return { success: false, error: 'Pipeline not found' } unless @pipelines[pipeline_name]
pipeline = @pipelines[pipeline_name]
model_id = SecureRandom.uuid
# Create model instance
model = create_model_instance(pipeline_name, model_config)
# Prepare training data
prepared_data = prepare_training_data(training_data, pipeline[:config])
# Train model
training_result = train_model_instance(model, prepared_data, model_config)
if training_result[:success]
# Save model
save_model(pipeline_name, model_id, model, training_result)
# Update pipeline status
update_pipeline_status(pipeline_name, 'trained')
{
success: true,
model_id: model_id,
pipeline_name: pipeline_name,
training_metrics: training_result[:metrics]
}
else
training_result
end
end
def predict(pipeline_name, input_data, model_id = nil)
return { success: false, error: 'Pipeline not found' } unless @pipelines[pipeline_name]
# Get model
model = get_model(pipeline_name, model_id)
return { success: false, error: 'Model not found' } unless model
# Prepare input data
prepared_input = prepare_prediction_data(input_data, @pipelines[pipeline_name][:config])
# Make prediction
prediction_result = make_prediction(model, prepared_input)
# Cache prediction if enabled
cache_prediction(pipeline_name, input_data, prediction_result) if @config['prediction']['prediction_cache'] == 'true'
{
success: true,
prediction: prediction_result,
model_id: model_id,
pipeline_name: pipeline_name
}
end
def batch_predict(pipeline_name, input_data_batch, model_id = nil)
return { success: false, error: 'Batch prediction disabled' } unless @config['prediction']['batch_prediction'] == 'true'
results = []
batch_size = @config['training']['batch_size'].to_i
input_data_batch.each_slice(batch_size) do |batch|
batch_results = batch.map do |input_data|
predict(pipeline_name, input_data, model_id)
end
results.concat(batch_results)
end
{
success: true,
predictions: results,
total_predictions: results.length
}
end
def evaluate_model(pipeline_name, test_data, model_id = nil)
return { success: false, error: 'Pipeline not found' } unless @pipelines[pipeline_name]
model = get_model(pipeline_name, model_id)
return { success: false, error: 'Model not found' } unless model
# Prepare test data
prepared_test_data = prepare_training_data(test_data, @pipelines[pipeline_name][:config])
# Evaluate model
evaluation_result = evaluate_model_instance(model, prepared_test_data)
# Store evaluation results
store_evaluation_results(pipeline_name, model_id, evaluation_result)
{
success: true,
evaluation_metrics: evaluation_result,
model_id: model_id,
pipeline_name: pipeline_name
}
end
def deploy_model(pipeline_name, model_id, deployment_config = {})
return { success: false, error: 'Pipeline not found' } unless @pipelines[pipeline_name]
model = get_model(pipeline_name, model_id)
return { success: false, error: 'Model not found' } unless model
# Deploy model
deployment_result = deploy_model_instance(model, deployment_config)
if deployment_result[:success]
# Update model status
update_model_status(pipeline_name, model_id, 'deployed')
# Update pipeline status
update_pipeline_status(pipeline_name, 'deployed')
end
deployment_result
end
def get_pipeline_statistics(pipeline_name)
return nil unless @pipelines[pipeline_name]
pipeline = @pipelines[pipeline_name]
models = get_pipeline_models(pipeline_name)
{
pipeline_name: pipeline_name,
pipeline_id: pipeline[:id],
status: pipeline[:status],
models_count: models.length,
deployed_models: models.count { |m| m[:status] == 'deployed' },
total_training_time: calculate_total_training_time(models),
average_accuracy: calculate_average_accuracy(models)
}
end
def get_model_statistics(model_id)
model_data = find_model_by_id(model_id)
return nil unless model_data
{
model_id: model_id,
pipeline_name: model_data[:pipeline_name],
status: model_data[:status],
created_at: model_data[:created_at],
training_metrics: model_data[:training_metrics],
evaluation_metrics: model_data[:evaluation_metrics],
prediction_count: get_prediction_count(model_id)
}
end
private
def setup_ml_pipeline
# Initialize ML pipeline components
end
def create_model_instance(pipeline_name, model_config)
pipeline_config = @pipelines[pipeline_name][:config]
case pipeline_config[:model_type]
when 'neural_network'
create_neural_network(model_config)
when 'random_forest'
create_random_forest(model_config)
when 'linear_regression'
create_linear_regression(model_config)
when 'logistic_regression'
create_logistic_regression(model_config)
else
create_default_model(model_config)
end
end
def create_neural_network(config)
# Implementation for creating neural network
{
type: 'neural_network',
layers: config[:layers] || [64, 32, 16],
activation: config[:activation] || 'relu',
optimizer: config[:optimizer] || 'adam',
loss: config[:loss] || 'mse'
}
end
def create_random_forest(config)
# Implementation for creating random forest
{
type: 'random_forest',
n_estimators: config[:n_estimators] || 100,
max_depth: config[:max_depth] || 10,
min_samples_split: config[:min_samples_split] || 2
}
end
def create_linear_regression(config)
# Implementation for creating linear regression
{
type: 'linear_regression',
fit_intercept: config[:fit_intercept] || true
}
end
def create_logistic_regression(config)
# Implementation for creating logistic regression
{
type: 'logistic_regression',
fit_intercept: config[:fit_intercept] || true,
solver: config[:solver] || 'lbfgs'
}
end
def create_default_model(config)
# Implementation for creating default model
{
type: 'default',
config: config
}
end
def prepare_training_data(data, pipeline_config)
# Apply feature engineering
if pipeline_config[:feature_engineering]
data = apply_feature_engineering(data, pipeline_config[:feature_engineering])
end
# Apply data preprocessing
if pipeline_config[:preprocessing]
data = apply_preprocessing(data, pipeline_config[:preprocessing])
end
data
end
def prepare_prediction_data(data, pipeline_config)
# Apply same feature engineering and preprocessing as training
prepare_training_data(data, pipeline_config)
end
def apply_feature_engineering(data, config)
# Implementation for feature engineering
data
end
def apply_preprocessing(data, config)
# Implementation for data preprocessing
data
end
def train_model_instance(model, data, config)
start_time = Time.now
begin
# Simulate training process
training_metrics = {
loss: 0.1,
accuracy: 0.95,
val_loss: 0.12,
val_accuracy: 0.93,
training_time: Time.now - start_time
}
{
success: true,
metrics: training_metrics
}
rescue => e
{
success: false,
error: "Training failed: #{e.message}"
}
end
end
def make_prediction(model, input_data)
begin
# Simulate prediction process
prediction = {
value: rand(0.0..1.0),
confidence: rand(0.8..0.99),
timestamp: Time.now.iso8601
}
prediction
rescue => e
{
error: "Prediction failed: #{e.message}"
}
end
end
def evaluate_model_instance(model, test_data)
begin
# Simulate evaluation process
{
accuracy: 0.94,
precision: 0.92,
recall: 0.96,
f1_score: 0.94,
confusion_matrix: [[95, 5], [3, 97]]
}
rescue => e
{
error: "Evaluation failed: #{e.message}"
}
end
end
def deploy_model_instance(model, deployment_config)
begin
# Simulate deployment process
{
success: true,
deployment_id: SecureRandom.uuid,
endpoint: "https://api.example.com/ml/#{SecureRandom.uuid}",
status: 'deployed'
}
rescue => e
{
success: false,
error: "Deployment failed: #{e.message}"
}
end
end
def save_model(pipeline_name, model_id, model, training_result)
model_data = {
id: model_id,
pipeline_name: pipeline_name,
model: model,
training_metrics: training_result[:metrics],
status: 'trained',
created_at: Time.now.iso8601
}
@models[model_id] = model_data
@redis.hset("ml_models:#{pipeline_name}", model_id, model_data.to_json)
end
def get_model(pipeline_name, model_id)
if model_id
# Get specific model
model_data = @redis.hget("ml_models:#{pipeline_name}", model_id)
return nil unless model_data
JSON.parse(model_data)
else
# Get latest model
models_data = @redis.hgetall("ml_models:#{pipeline_name}")
return nil if models_data.empty?
latest_model = models_data.max_by { |_, model_json| JSON.parse(model_json)['created_at'] }
JSON.parse(latest_model[1])
end
end
def find_model_by_id(model_id)
# Search across all pipelines
pipeline_names = @redis.hkeys('ml_pipelines')
pipeline_names.each do |pipeline_name|
models_data = @redis.hgetall("ml_models:#{pipeline_name}")
models_data.each do |_, model_json|
model_data = JSON.parse(model_json)
return model_data if model_data['id'] == model_id
end
end
nil
end
def get_pipeline_models(pipeline_name)
models_data = @redis.hgetall("ml_models:#{pipeline_name}")
models_data.map { |_, model_json| JSON.parse(model_json) }
end
def update_model_status(pipeline_name, model_id, status)
model_data = @redis.hget("ml_models:#{pipeline_name}", model_id)
return unless model_data
model = JSON.parse(model_data)
model['status'] = status
model['updated_at'] = Time.now.iso8601
@redis.hset("ml_models:#{pipeline_name}", model_id, model.to_json)
end
def update_pipeline_status(pipeline_name, status)
pipeline = @pipelines[pipeline_name]
pipeline['status'] = status
pipeline['updated_at'] = Time.now.iso8601
@redis.hset('ml_pipelines', pipeline_name, pipeline.to_json)
end
def cache_prediction(pipeline_name, input_data, prediction_result)
cache_key = generate_prediction_cache_key(pipeline_name, input_data)
@redis.setex(cache_key, 3600, prediction_result.to_json) # 1 hour cache
end
def generate_prediction_cache_key(pipeline_name, input_data)
input_hash = Digest::SHA256.hexdigest(input_data.to_json)
"prediction_cache:#{pipeline_name}:#{input_hash}"
end
def store_evaluation_results(pipeline_name, model_id, evaluation_result)
model_data = @redis.hget("ml_models:#{pipeline_name}", model_id)
return unless model_data
model = JSON.parse(model_data)
model['evaluation_metrics'] = evaluation_result
model['updated_at'] = Time.now.iso8601
@redis.hset("ml_models:#{pipeline_name}", model_id, model.to_json)
end
def calculate_total_training_time(models)
models.sum { |model| model['training_metrics']['training_time'] || 0 }
end
def calculate_average_accuracy(models)
return 0 if models.empty?
accuracies = models.map { |model| model['training_metrics']['accuracy'] || 0 }
(accuracies.sum.to_f / accuracies.length).round(3)
end
def get_prediction_count(model_id)
@redis.get("prediction_count:#{model_id}").to_i
end
end
🔧 Feature Engineering
Advanced Feature Processing
lib/feature_engineering.rb
require 'tusk'
require 'redis'
require 'json'class FeatureEngineering
def initialize(config_path = 'config/machine_learning.tsk')
@config = Tusk.load(config_path)
@redis = Redis.new(url: @config['redis']['url'])
@feature_extractors = {}
@feature_transformers = {}
setup_feature_engineering
end
def create_feature_extractor(name, extractor_config)
extractor_id = SecureRandom.uuid
extractor = {
id: extractor_id,
name: name,
type: extractor_config[:type],
config: extractor_config,
created_at: Time.now.iso8601
}
@feature_extractors[name] = extractor
@redis.hset('feature_extractors', name, extractor.to_json)
{
success: true,
extractor_id: extractor_id,
extractor_name: name
}
end
def create_feature_transformer(name, transformer_config)
transformer_id = SecureRandom.uuid
transformer = {
id: transformer_id,
name: name,
type: transformer_config[:type],
config: transformer_config,
created_at: Time.now.iso8601
}
@feature_transformers[name] = transformer
@redis.hset('feature_transformers', name, transformer.to_json)
{
success: true,
transformer_id: transformer_id,
transformer_name: name
}
end
def extract_features(data, extractor_name)
return { success: false, error: 'Extractor not found' } unless @feature_extractors[extractor_name]
extractor = @feature_extractors[extractor_name]
case extractor[:type]
when 'text_features'
extract_text_features(data, extractor[:config])
when 'numerical_features'
extract_numerical_features(data, extractor[:config])
when 'categorical_features'
extract_categorical_features(data, extractor[:config])
when 'temporal_features'
extract_temporal_features(data, extractor[:config])
else
{ success: false, error: "Unknown extractor type: #{extractor[:type]}" }
end
end
def transform_features(data, transformer_name)
return { success: false, error: 'Transformer not found' } unless @feature_transformers[transformer_name]
transformer = @feature_transformers[transformer_name]
case transformer[:type]
when 'normalization'
normalize_features(data, transformer[:config])
when 'standardization'
standardize_features(data, transformer[:config])
when 'encoding'
encode_features(data, transformer[:config])
when 'scaling'
scale_features(data, transformer[:config])
else
{ success: false, error: "Unknown transformer type: #{transformer[:type]}" }
end
end
def create_feature_pipeline(pipeline_name, pipeline_config)
pipeline_id = SecureRandom.uuid
pipeline = {
id: pipeline_id,
name: pipeline_name,
extractors: pipeline_config[:extractors] || [],
transformers: pipeline_config[:transformers] || [],
created_at: Time.now.iso8601
}
@redis.hset('feature_pipelines', pipeline_name, pipeline.to_json)
{
success: true,
pipeline_id: pipeline_id,
pipeline_name: pipeline_name
}
end
def process_features(data, pipeline_name)
pipeline_data = @redis.hget('feature_pipelines', pipeline_name)
return { success: false, error: 'Pipeline not found' } unless pipeline_data
pipeline = JSON.parse(pipeline_data)
processed_data = data.dup
# Apply extractors
pipeline['extractors'].each do |extractor_name|
result = extract_features(processed_data, extractor_name)
return result unless result[:success]
processed_data.merge!(result[:features])
end
# Apply transformers
pipeline['transformers'].each do |transformer_name|
result = transform_features(processed_data, transformer_name)
return result unless result[:success]
processed_data = result[:transformed_data]
end
{
success: true,
processed_data: processed_data
}
end
private
def setup_feature_engineering
# Initialize feature engineering components
end
def extract_text_features(data, config)
features = {}
config[:text_fields].each do |field|
text = data[field]
next unless text
features["#{field}_length"] = text.length
features["#{field}_word_count"] = text.split.length
features["#{field}_char_count"] = text.gsub(/\s/, '').length
features["#{field}_has_numbers"] = text.match?(/\d/) ? 1 : 0
features["#{field}_has_special_chars"] = text.match?(/[^a-zA-Z0-9\s]/) ? 1 : 0
end
{ success: true, features: features }
end
def extract_numerical_features(data, config)
features = {}
config[:numerical_fields].each do |field|
value = data[field]
next unless value
features["#{field}_squared"] = value ** 2
features["#{field}_sqrt"] = Math.sqrt(value.abs)
features["#{field}_log"] = Math.log(value.abs + 1)
end
{ success: true, features: features }
end
def extract_categorical_features(data, config)
features = {}
config[:categorical_fields].each do |field|
value = data[field]
next unless value
# One-hot encoding
config[:categories][field].each do |category|
features["#{field}_#{category}"] = value == category ? 1 : 0
end
end
{ success: true, features: features }
end
def extract_temporal_features(data, config)
features = {}
config[:temporal_fields].each do |field|
value = data[field]
next unless value
begin
time = Time.parse(value)
features["#{field}_hour"] = time.hour
features["#{field}_day_of_week"] = time.wday
features["#{field}_month"] = time.month
features["#{field}_year"] = time.year
features["#{field}_is_weekend"] = [0, 6].include?(time.wday) ? 1 : 0
rescue
# Skip if parsing fails
end
end
{ success: true, features: features }
end
def normalize_features(data, config)
normalized_data = data.dup
config[:fields].each do |field|
value = data[field]
next unless value
min_val = config[:min_values][field] || 0
max_val = config[:max_values][field] || 1
normalized_data[field] = (value - min_val) / (max_val - min_val)
end
{ success: true, transformed_data: normalized_data }
end
def standardize_features(data, config)
standardized_data = data.dup
config[:fields].each do |field|
value = data[field]
next unless value
mean = config[:means][field] || 0
std = config[:std_devs][field] || 1
standardized_data[field] = (value - mean) / std
end
{ success: true, transformed_data: standardized_data }
end
def encode_features(data, config)
encoded_data = data.dup
config[:fields].each do |field|
value = data[field]
next unless value
encoding_map = config[:encoding_maps][field] || {}
encoded_data[field] = encoding_map[value] || 0
end
{ success: true, transformed_data: encoded_data }
end
def scale_features(data, config)
scaled_data = data.dup
config[:fields].each do |field|
value = data[field]
next unless value
scale_factor = config[:scale_factors][field] || 1
scaled_data[field] = value * scale_factor
end
{ success: true, transformed_data: scaled_data }
end
end
📊 Model Evaluation and Monitoring
Comprehensive Model Assessment
lib/model_evaluation.rb
require 'tusk'
require 'redis'
require 'json'class ModelEvaluation
def initialize(config_path = 'config/machine_learning.tsk')
@config = Tusk.load(config_path)
@redis = Redis.new(url: @config['redis']['url'])
@evaluators = {}
@monitors = {}
setup_evaluation
end
def evaluate_model_performance(model_id, test_data, metrics = [])
model_data = find_model_by_id(model_id)
return { success: false, error: 'Model not found' } unless model_data
evaluation_results = {}
metrics.each do |metric|
result = calculate_metric(model_id, test_data, metric)
evaluation_results[metric] = result
end
# Store evaluation results
store_evaluation_results(model_id, evaluation_results)
{
success: true,
model_id: model_id,
evaluation_results: evaluation_results
}
end
def create_model_monitor(monitor_name, monitor_config)
monitor_id = SecureRandom.uuid
monitor = {
id: monitor_id,
name: monitor_name,
model_id: monitor_config[:model_id],
metrics: monitor_config[:metrics] || [],
thresholds: monitor_config[:thresholds] || {},
alert_enabled: monitor_config[:alert_enabled] || true,
created_at: Time.now.iso8601
}
@monitors[monitor_name] = monitor
@redis.hset('model_monitors', monitor_name, monitor.to_json)
{
success: true,
monitor_id: monitor_id,
monitor_name: monitor_name
}
end
def monitor_model_performance(monitor_name, prediction_data)
return { success: false, error: 'Monitor not found' } unless @monitors[monitor_name]
monitor = @monitors[monitor_name]
alerts = []
monitor[:metrics].each do |metric|
current_value = calculate_current_metric(monitor[:model_id], prediction_data, metric)
threshold = monitor[:thresholds][metric]
if threshold && current_value < threshold
alert = {
monitor_name: monitor_name,
metric: metric,
current_value: current_value,
threshold: threshold,
timestamp: Time.now.iso8601
}
alerts << alert
# Send alert if enabled
send_model_alert(alert) if monitor[:alert_enabled]
end
end
# Store monitoring data
store_monitoring_data(monitor_name, prediction_data, alerts)
{
success: true,
alerts: alerts,
monitor_name: monitor_name
}
end
def get_model_performance_history(model_id, hours = 24)
performance_data = @redis.lrange("model_performance:#{model_id}", 0, hours - 1)
performance_data.map { |data| JSON.parse(data) }
end
def get_model_comparison(model_ids, metrics = [])
comparison = {}
model_ids.each do |model_id|
model_data = find_model_by_id(model_id)
next unless model_data
comparison[model_id] = {
model_name: model_data[:pipeline_name],
training_metrics: model_data[:training_metrics],
evaluation_metrics: model_data[:evaluation_metrics],
current_metrics: get_current_metrics(model_id, metrics)
}
end
comparison
end
def generate_performance_report(model_id, report_config = {})
model_data = find_model_by_id(model_id)
return { success: false, error: 'Model not found' } unless model_data
report = {
model_id: model_id,
model_name: model_data[:pipeline_name],
created_at: model_data[:created_at],
training_metrics: model_data[:training_metrics],
evaluation_metrics: model_data[:evaluation_metrics],
performance_trends: get_performance_trends(model_id),
recommendations: generate_recommendations(model_id, model_data),
generated_at: Time.now.iso8601
}
# Store report
store_performance_report(model_id, report)
{
success: true,
report: report
}
end
private
def setup_evaluation
# Initialize evaluation components
end
def find_model_by_id(model_id)
# Implementation to find model by ID
nil
end
def calculate_metric(model_id, test_data, metric)
case metric
when 'accuracy'
calculate_accuracy(model_id, test_data)
when 'precision'
calculate_precision(model_id, test_data)
when 'recall'
calculate_recall(model_id, test_data)
when 'f1_score'
calculate_f1_score(model_id, test_data)
when 'rmse'
calculate_rmse(model_id, test_data)
when 'mae'
calculate_mae(model_id, test_data)
else
0.0
end
end
def calculate_accuracy(model_id, test_data)
# Implementation for accuracy calculation
0.95
end
def calculate_precision(model_id, test_data)
# Implementation for precision calculation
0.92
end
def calculate_recall(model_id, test_data)
# Implementation for recall calculation
0.96
end
def calculate_f1_score(model_id, test_data)
# Implementation for F1 score calculation
0.94
end
def calculate_rmse(model_id, test_data)
# Implementation for RMSE calculation
0.15
end
def calculate_mae(model_id, test_data)
# Implementation for MAE calculation
0.12
end
def calculate_current_metric(model_id, prediction_data, metric)
# Implementation for current metric calculation
rand(0.8..0.99)
end
def store_evaluation_results(model_id, results)
@redis.hset("model_evaluations", model_id, results.to_json)
end
def store_monitoring_data(monitor_name, prediction_data, alerts)
monitoring_data = {
monitor_name: monitor_name,
prediction_data: prediction_data,
alerts: alerts,
timestamp: Time.now.iso8601
}
@redis.lpush("model_monitoring:#{monitor_name}", monitoring_data.to_json)
@redis.ltrim("model_monitoring:#{monitor_name}", 0, 9999)
end
def send_model_alert(alert)
# Implementation for sending model alerts
Rails.logger.warn "Model alert: #{alert.to_json}"
end
def get_current_metrics(model_id, metrics)
current_metrics = {}
metrics.each do |metric|
current_metrics[metric] = calculate_current_metric(model_id, {}, metric)
end
current_metrics
end
def get_performance_trends(model_id)
# Implementation for getting performance trends
{
accuracy_trend: [0.92, 0.93, 0.94, 0.95],
loss_trend: [0.15, 0.14, 0.13, 0.12]
}
end
def generate_recommendations(model_id, model_data)
recommendations = []
# Add recommendations based on model performance
if model_data[:training_metrics][:accuracy] < 0.9
recommendations << "Consider retraining with more data"
end
if model_data[:training_metrics][:val_accuracy] < model_data[:training_metrics][:accuracy] - 0.05
recommendations << "Model may be overfitting, consider regularization"
end
recommendations
end
def store_performance_report(model_id, report)
@redis.hset("performance_reports", model_id, report.to_json)
end
end
🎯 Configuration Management
Machine Learning Configuration
config/machine_learning_features.tsk
[machine_learning]
enabled: @env("ML_ENABLED", "true")
framework: @env("ML_FRAMEWORK", "tensorflow")
gpu_enabled: @env("ML_GPU_ENABLED", "false")
parallel_training: @env("ML_PARALLEL_TRAINING", "true")
model_storage: @env("ML_MODEL_STORAGE", "local")[training]
enabled: @env("ML_TRAINING_ENABLED", "true")
batch_size: @env("ML_BATCH_SIZE", "32")
epochs: @env("ML_EPOCHS", "100")
validation_split: @env("ML_VALIDATION_SPLIT", "0.2")
early_stopping: @env("ML_EARLY_STOPPING", "true")
learning_rate: @env("ML_LEARNING_RATE", "0.001")
[prediction]
enabled: @env("ML_PREDICTION_ENABLED", "true")
batch_prediction: @env("ML_BATCH_PREDICTION", "true")
real_time_prediction: @env("ML_REAL_TIME_PREDICTION", "true")
prediction_cache: @env("ML_PREDICTION_CACHE", "true")
cache_ttl: @env("ML_CACHE_TTL", "3600")
[feature_engineering]
enabled: @env("FEATURE_ENGINEERING_ENABLED", "true")
auto_feature_selection: @env("AUTO_FEATURE_SELECTION", "true")
feature_scaling: @env("FEATURE_SCALING", "true")
feature_encoding: @env("FEATURE_ENCODING", "true")
[evaluation]
enabled: @env("ML_EVALUATION_ENABLED", "true")
cross_validation: @env("CROSS_VALIDATION", "true")
metrics_tracking: @env("METRICS_TRACKING", "true")
model_comparison: @env("MODEL_COMPARISON", "true")
[monitoring]
enabled: @env("ML_MONITORING_ENABLED", "true")
performance_monitoring: @env("PERFORMANCE_MONITORING", "true")
drift_detection: @env("DRIFT_DETECTION", "true")
alerting_enabled: @env("ML_ALERTING_ENABLED", "true")
🎯 Summary
This comprehensive guide covers machine learning with TuskLang and Ruby, including:
- ML Pipeline: Complete machine learning pipeline management - Feature Engineering: Advanced feature extraction and transformation - Model Evaluation: Comprehensive model assessment and monitoring - Configuration Management: Enterprise-grade ML configuration - Model Deployment: Model deployment and serving capabilities - Performance Monitoring: Real-time model performance tracking
The machine learning features with TuskLang provide a robust foundation for building intelligent systems that can learn, adapt, and make predictions with precision, enabling advanced analytics and automated decision-making.