🧠 Advanced Machine Learning Integration with TuskLang & Go

Introduction

TuskLang and Go together unlock a new era of configuration-driven machine learning. Forget YAML hell and brittle pipelines—define, deploy, and monitor ML models with live database queries, @ operators, and Go’s concurrency. This is how rebels do ML.

Key Features

- Config-driven ML pipelines - Real-time model serving - A/B testing and canary deployments - GPU acceleration - Database-driven ML - Model monitoring and metrics - Security and privacy

Example: ML Pipeline in TuskLang

[pipeline]
model: @file.read("models/iris.onnx")
preprocess: @go("ml.Preprocess")
predict: @go("ml.Predict")
metrics: @metrics("inference_latency_ms", 0)
cache: @cache("10m", "ml_predictions")

Go: Model Serving Example

package ml
import (
  "github.com/goml/gobrain"
  "net/http"
)
func Predict(input []float64) float64 {
  // Load model, run prediction
}

Real-Time A/B Testing

[ab_test]
variant_a: @go("ml.PredictA")
variant_b: @go("ml.PredictB")
route: @learn("best_variant", "a")

GPU Acceleration

- Use Go CUDA bindings (e.g., gorgonia.org/cu) - TuskLang config: gpu: @env("USE_GPU", false)

Database-Driven ML

[data]
train_query: @query("SELECT * FROM training_data")

Model Monitoring

[monitoring]
latency: @metrics("inference_latency_ms", 0)
accuracy: @metrics("model_accuracy", 0.95)

Security & Privacy

- Use @encrypt for sensitive data - Secure model files with @env.secure

Best Practices

- Use TuskLang for all pipeline config - Monitor with @metrics - Secure with @env.secure and @encrypt - Use Go’s concurrency for real-time serving

Troubleshooting

- Check Go logs for model errors - Use TuskLang’s @cache to avoid repeated expensive inference

Conclusion

TuskLang + Go = ML pipelines that are fast, secure, and easy to manage. No YAML, no drama. Just results.