Machine Learning Integration with TuskLang

🧠 Revolutionary ML - Where Intelligence Meets Configuration

TuskLang transforms machine learning from a complex, code-heavy process into an intelligent, configuration-driven system that adapts to your data and business needs. No more fighting with ML frameworks - TuskLang brings the power of intelligent learning to your fingertips.

"We don't bow to any king" - especially not to bloated ML frameworks that require PhDs to operate.

🎯 Core ML Capabilities

Intelligent Model Training

#!/bin/bash
TuskLang-powered ML training pipeline
source tusk.sh
Dynamic model training with intelligent hyperparameter optimization
training_config="
[model_training]
algorithm: @learn('optimal_algorithm', 'random_forest')
hyperparameters: @optimize('hyperparams', 'auto')
training_data: @env('TRAINING_DATA_PATH')
validation_split: @learn('validation_ratio', 0.2)
[feature_engineering]
feature_selection: @ml.select_features('correlation_threshold')
feature_scaling: @ml.scale_features('standard_scaler')
feature_creation: @file.read('feature_rules.tsk')
[model_evaluation]
metrics: @ml.evaluate('accuracy_precision_recall')
cross_validation: @ml.cross_validate('k_fold', 5)
performance_tracking: @metrics.model('training_performance')
"
Execute intelligent model training
tsk ml train --config <(echo "$training_config") --auto-optimize

Real-Time Prediction Engine

#!/bin/bash
Real-time ML prediction with TuskLang
prediction_config="
[prediction_engine]
model_path: @env('MODEL_PATH', '/models/production')
prediction_endpoint: @env('PREDICTION_URL', 'http://localhost:8080/predict')
batch_size: @optimize('prediction_batch', 100)
[prediction_pipeline]
preprocessing: @ml.preprocess('input_data')
feature_extraction: @ml.extract_features('raw_input')
prediction: @ml.predict('processed_features')
postprocessing: @ml.postprocess('predictions')
[prediction_monitoring]
accuracy_tracking: @metrics.prediction('accuracy_score')
latency_monitoring: @metrics.latency('prediction_time')
drift_detection: @ml.detect_drift('data_distribution')
"
Start real-time prediction service
tsk ml predict --config <(echo "$prediction_config") --serve

🎓 Model Training and Development

Automated Model Training

#!/bin/bash
Automated ML model training pipeline
auto_training_config="
[automated_training]
data_preparation:
  cleaning: @ml.clean_data('missing_values')
  preprocessing: @ml.preprocess('data_types')
  validation: @validate.data('quality_checks')
model_selection:
  algorithms: @file.read('algorithm_candidates.tsk')
  evaluation: @ml.evaluate_models('performance_metrics')
  selection: @ml.select_best('accuracy_score')
hyperparameter_tuning:
  method: @learn('tuning_method', 'bayesian_optimization')
  search_space: @file.read('hyperparam_space.tsk')
  optimization: @optimize.hyperparams('model_performance')
"
Run automated training
tsk ml auto-train --config <(echo "$auto_training_config") --full-pipeline

Feature Engineering Automation

#!/bin/bash
Intelligent feature engineering
feature_config="
[feature_engineering]
automatic_features:
  temporal: @ml.create_temporal('date_features')
  categorical: @ml.encode_categorical('one_hot_encoding')
  numerical: @ml.transform_numerical('log_scaling')
feature_selection:
  correlation: @ml.select_correlation('threshold', 0.8)
  importance: @ml.select_importance('feature_ranking')
  dimensionality: @ml.reduce_dimensions('pca')
feature_monitoring:
  drift_detection: @ml.detect_feature_drift('distribution_changes')
  importance_tracking: @metrics.feature('importance_scores')
  quality_assessment: @ml.assess_features('quality_metrics')
"
Execute feature engineering
tsk ml features --config <(echo "$feature_config") --engineer

🔮 Prediction and Inference

Batch Prediction Processing

#!/bin/bash
Batch prediction with intelligent batching
batch_prediction_config="
[batch_prediction]
input_data: @env('BATCH_INPUT_PATH')
output_path: @env('PREDICTION_OUTPUT_PATH')
batch_size: @optimize('optimal_batch_size', 1000)
[prediction_pipeline]
data_loading: @ml.load_batch('input_files')
preprocessing: @ml.preprocess_batch('data_cleaning')
prediction: @ml.predict_batch('model_inference')
postprocessing: @ml.postprocess_batch('result_formatting')
[performance_optimization]
parallel_processing: @parallel.predictions('multi_core')
memory_optimization: @optimize.memory('batch_processing')
caching: @cache.predictions('intermediate_results')
"
Execute batch predictions
tsk ml batch-predict --config <(echo "$batch_prediction_config") --optimize

Real-Time Inference API

#!/bin/bash
Real-time inference API with TuskLang
inference_config="
[inference_api]
endpoint: @env('API_ENDPOINT', '/api/v1/predict')
model_serving: @ml.serve_model('production_model')
load_balancing: @load_balance('prediction_requests')
[request_processing]
input_validation: @validate.input('data_schema')
preprocessing: @ml.preprocess_request('input_data')
prediction: @ml.inference('model_prediction')
response_formatting: @ml.format_response('prediction_output')
[api_monitoring]
request_tracking: @metrics.api('request_count')
latency_monitoring: @metrics.latency('response_time')
error_tracking: @metrics.errors('prediction_errors')
"
Start inference API
tsk ml serve --config <(echo "$inference_config") --api

📊 Model Management and Operations

Model Versioning and Deployment

#!/bin/bash
Intelligent model versioning and deployment
model_ops_config="
[model_versioning]
version_control: @version.model('model_versions')
experiment_tracking: @ml.track_experiments('mlflow_integration')
model_registry: @registry.models('model_catalog')
[model_deployment]
staging_deployment: @deploy.staging('model_testing')
production_deployment: @deploy.production('model_rollout')
rollback_capability: @deploy.rollback('previous_version')
[deployment_monitoring]
health_checks: @health.model('model_availability')
performance_monitoring: @monitor.model('prediction_performance')
resource_usage: @metrics.resources('model_resources')
"
Manage model lifecycle
tsk ml deploy --config <(echo "$model_ops_config") --version-control

Model Performance Monitoring

#!/bin/bash
Comprehensive model performance monitoring
performance_config="
[performance_monitoring]
accuracy_tracking: @metrics.accuracy('prediction_accuracy')
precision_recall: @metrics.precision_recall('classification_metrics')
regression_metrics: @metrics.regression('mse_mae')
[drift_detection]
data_drift: @ml.detect_data_drift('feature_distributions')
concept_drift: @ml.detect_concept_drift('prediction_patterns')
model_decay: @ml.detect_decay('performance_degradation')
[alerting]
performance_alerts: @alert.performance('accuracy_threshold')
drift_alerts: @alert.drift('drift_detected')
resource_alerts: @alert.resources('resource_usage')
"
Monitor model performance
tsk ml monitor --config <(echo "$performance_config") --track-metrics

🔄 ML Pipeline Automation

End-to-End ML Pipeline

#!/bin/bash
Complete ML pipeline automation
pipeline_config="
[ml_pipeline]
data_ingestion:
  sources: @env('DATA_SOURCES')
  validation: @validate.data('quality_checks')
  preprocessing: @ml.preprocess('data_cleaning')
feature_engineering:
  feature_creation: @ml.create_features('feature_rules')
  feature_selection: @ml.select_features('importance_threshold')
  feature_scaling: @ml.scale_features('normalization')
model_training:
  algorithm_selection: @ml.select_algorithm('performance_comparison')
  hyperparameter_tuning: @ml.tune_hyperparams('optimization')
  model_evaluation: @ml.evaluate_model('validation_metrics')
model_deployment:
  model_serving: @ml.serve_model('production_deployment')
  performance_monitoring: @ml.monitor_performance('metrics_tracking')
  retraining_scheduling: @ml.schedule_retraining('performance_threshold')
"
Execute complete ML pipeline
tsk ml pipeline --config <(echo "$pipeline_config") --end-to-end

Automated Retraining

#!/bin/bash
Intelligent model retraining
retraining_config="
[retraining_triggers]
performance_degradation: @ml.trigger_retraining('accuracy_threshold')
data_drift: @ml.trigger_retraining('drift_detection')
time_based: @ml.trigger_retraining('scheduled_retraining')
[retraining_process]
data_collection: @ml.collect_new_data('recent_data')
model_retraining: @ml.retrain_model('updated_dataset')
performance_comparison: @ml.compare_models('old_vs_new')
[deployment_strategy]
a_b_testing: @ml.ab_test('model_comparison')
gradual_rollout: @ml.gradual_rollout('percentage_increase')
full_deployment: @ml.full_deployment('complete_switch')
"
Set up automated retraining
tsk ml retrain --config <(echo "$retraining_config") --automated

🎯 Specialized ML Applications

Natural Language Processing

#!/bin/bash
NLP pipeline with TuskLang
nlp_config="
[nlp_pipeline]
text_preprocessing:
  cleaning: @nlp.clean_text('remove_noise')
  tokenization: @nlp.tokenize('word_tokens')
  normalization: @nlp.normalize('text_standardization')
feature_extraction:
  embeddings: @nlp.embeddings('word_vectors')
  tf_idf: @nlp.tfidf('document_features')
  sentiment: @nlp.sentiment('emotion_analysis')
model_training:
  classification: @nlp.train_classifier('text_classification')
  generation: @nlp.train_generator('text_generation')
  translation: @nlp.train_translator('language_translation')
"
Execute NLP pipeline
tsk ml nlp --config <(echo "$nlp_config") --process-text

Computer Vision

#!/bin/bash
Computer vision pipeline
vision_config="
[vision_pipeline]
image_preprocessing:
  resizing: @cv.resize('target_dimensions')
  normalization: @cv.normalize('pixel_values')
  augmentation: @cv.augment('data_augmentation')
feature_extraction:
  cnn_features: @cv.cnn_features('convolutional_layers')
  object_detection: @cv.detect_objects('bounding_boxes')
  segmentation: @cv.segment_image('pixel_classification')
model_training:
  classification: @cv.train_classifier('image_classification')
  detection: @cv.train_detector('object_detection')
  segmentation: @cv.train_segmenter('semantic_segmentation')
"
Execute computer vision pipeline
tsk ml vision --config <(echo "$vision_config") --process-images

Time Series Forecasting

#!/bin/bash
Time series forecasting
timeseries_config="
[timeseries_forecasting]
data_preparation:
  seasonality: @ts.detect_seasonality('seasonal_patterns')
  trend_analysis: @ts.analyze_trend('trend_components')
  stationarity: @ts.check_stationarity('stationarity_test')
model_training:
  arima: @ts.train_arima('autoregressive_model')
  lstm: @ts.train_lstm('neural_forecasting')
  prophet: @ts.train_prophet('facebook_prophet')
forecasting:
  short_term: @ts.forecast_short('next_24_hours')
  medium_term: @ts.forecast_medium('next_week')
  long_term: @ts.forecast_long('next_month')
"
Execute time series forecasting
tsk ml timeseries --config <(echo "$timeseries_config") --forecast

🔧 Advanced ML Features

Ensemble Learning

#!/bin/bash
Ensemble learning with multiple models
ensemble_config="
[ensemble_learning]
base_models:
  model1: @ml.train_model('random_forest')
  model2: @ml.train_model('gradient_boosting')
  model3: @ml.train_model('neural_network')
ensemble_methods:
  voting: @ml.ensemble_voting('majority_vote')
  stacking: @ml.ensemble_stacking('meta_learner')
  blending: @ml.ensemble_blending('weighted_average')
performance_optimization:
  model_selection: @ml.select_ensemble('best_combination')
  weight_optimization: @ml.optimize_weights('ensemble_weights')
  diversity_measurement: @ml.measure_diversity('model_diversity')
"
Create ensemble model
tsk ml ensemble --config <(echo "$ensemble_config") --combine

Transfer Learning

#!/bin/bash
Transfer learning for domain adaptation
transfer_config="
[transfer_learning]
pre_trained_models:
  source_model: @ml.load_pretrained('imagenet_model')
  target_domain: @env('TARGET_DOMAIN_DATA')
  adaptation_method: @ml.adapt_model('fine_tuning')
adaptation_strategy:
  feature_extraction: @ml.extract_features('frozen_layers')
  fine_tuning: @ml.fine_tune('trainable_layers')
  domain_adaptation: @ml.adapt_domain('domain_alignment')
performance_evaluation:
  source_performance: @ml.evaluate_source('source_metrics')
  target_performance: @ml.evaluate_target('target_metrics')
  adaptation_effectiveness: @ml.evaluate_adaptation('improvement')
"
Execute transfer learning
tsk ml transfer --config <(echo "$transfer_config") --adapt

🛠️ ML Operations and Monitoring

MLOps Pipeline

#!/bin/bash
Complete MLOps pipeline
mlops_config="
[mlops_pipeline]
development:
  experiment_tracking: @mlops.track_experiments('mlflow')
  version_control: @mlops.version_models('git_lfs')
  testing: @mlops.test_models('unit_integration')
deployment:
  containerization: @mlops.containerize('docker_images')
  orchestration: @mlops.orchestrate('kubernetes')
  monitoring: @mlops.monitor('prometheus_grafana')
operations:
  scaling: @mlops.scale('auto_scaling')
  backup: @mlops.backup('model_backups')
  disaster_recovery: @mlops.recovery('failover_strategy')
"
Execute MLOps pipeline
tsk mlops --config <(echo "$mlops_config") --full-pipeline

Model Explainability

#!/bin/bash
Model explainability and interpretability
explainability_config="
[model_explainability]
feature_importance:
  permutation_importance: @explain.permutation('feature_ranking')
  shap_values: @explain.shap('feature_contributions')
  lime_explanations: @explain.lime('local_explanations')
interpretability:
  decision_trees: @explain.decision_tree('tree_structure')
  rule_extraction: @explain.extract_rules('if_then_rules')
  counterfactuals: @explain.counterfactuals('what_if_scenarios')
visualization:
  feature_plots: @explain.plot_features('importance_charts')
  partial_dependence: @explain.partial_dependence('feature_effects')
  interaction_plots: @explain.interactions('feature_interactions')
"
Generate model explanations
tsk ml explain --config <(echo "$explainability_config") --interpret

🔒 ML Security and Privacy

Privacy-Preserving ML

#!/bin/bash
Privacy-preserving machine learning
privacy_config="
[privacy_preserving_ml]
differential_privacy:
  noise_addition: @privacy.add_noise('gaussian_noise')
  privacy_budget: @privacy.budget('epsilon_delta')
  composition_theorems: @privacy.compose('budget_management')
federated_learning:
  distributed_training: @privacy.federated('local_training')
  secure_aggregation: @privacy.secure_aggregate('encrypted_aggregation')
  privacy_guarantees: @privacy.guarantees('privacy_protection')
homomorphic_encryption:
  encrypted_computation: @privacy.homomorphic('encrypted_ml')
  secure_inference: @privacy.secure_inference('encrypted_predictions')
  key_management: @privacy.key_management('encryption_keys')
"
Apply privacy-preserving techniques
tsk ml privacy --config <(echo "$privacy_config") --protect

📚 Best Practices and Patterns

ML Design Patterns

#!/bin/bash
Common ML design patterns
ml_patterns_config="
[design_patterns]
data_pipeline:
  etl_pattern: @pattern.etl('extract_transform_load')
  streaming_pattern: @pattern.streaming('real_time_processing')
  batch_pattern: @pattern.batch('batch_processing')
model_patterns:
  ensemble_pattern: @pattern.ensemble('multiple_models')
  transfer_pattern: @pattern.transfer('knowledge_transfer')
  active_learning: @pattern.active_learning('human_in_loop')
deployment_patterns:
  canary_deployment: @pattern.canary('gradual_rollout')
  blue_green: @pattern.blue_green('zero_downtime')
  shadow_deployment: @pattern.shadow('traffic_mirroring')
"
Apply ML design patterns
tsk ml patterns --config <(echo "$ml_patterns_config") --apply

🚀 Getting Started with ML

Quick Start Example

#!/bin/bash
Simple ML example with TuskLang
simple_ml_config="
[simple_classification]
data:
  input: 'iris_dataset.csv'
  target: 'species'
  split: 0.8
model:
  algorithm: 'random_forest'
  hyperparameters: |
    n_estimators: 100
    max_depth: 10
    random_state: 42
training:
  cross_validation: 5
  metrics: ['accuracy', 'precision', 'recall']
  save_model: 'iris_classifier.pkl'
prediction:
  new_data: 'new_iris_data.csv'
  output: 'predictions.csv'
"
Run simple ML pipeline
tsk ml quick-start --config <(echo "$simple_ml_config") --execute

📖 Related Documentation

- Data Pipeline Integration: 097-data-pipeline-bash.md - @ Operator System: 031-sql-operator-bash.md - Performance Optimization: 086-error-handling-bash.md - Monitoring Integration: 083-monitoring-integration-bash.md - Event-Driven Architecture: 094-event-driven-architecture-bash.md

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Ready to revolutionize your machine learning workflows with TuskLang's intelligent ML capabilities?