{"id":3532,"date":"2025-11-25T11:12:58","date_gmt":"2025-11-25T10:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/model-deployment\/"},"modified":"2026-08-26T11:25:40","modified_gmt":"2026-08-26T09:25:40","slug":"model-deployment","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\/","title":{"rendered":"Machine learning tutorial: Model deployment"},"content":{"rendered":"<style>.ndg{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 12px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndg *{box-sizing:border-box}.ndg a{text-decoration:none}.ndg-wrap{width:min(100%,1000px);margin:0 auto}.ndg-lead{font-size:19px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 24px}.ndg-lead a{color:#2d799f;font-weight:600}.ndg-herofig{margin:0 0 30px;text-align:center}.ndg-herofig img{max-width:min(440px,100%);height:auto}.ndg-eyebrow{margin:0 0 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12px 28px rgba(0,18,51,.12)!important}.ndg-step__body img[src$=\".svg\"],.ndg-step__body img[data-src$=\".svg\"]{display:block;margin:18px auto;max-width:min(520px,100%);height:auto}.ndg-step__body table{border-collapse:separate;border-spacing:0;margin:20px 0;font-size:15px;width:auto;max-width:100%;background:#fbfcfd;border-radius:12px;overflow:hidden;box-shadow:0 10px 24px rgba(0,18,51,.08)}.ndg-step__body th,.ndg-step__body td{padding:11px 18px;border-bottom:1px solid #e6ecf0;text-align:left}.ndg-step__body th{background:#e9f1f6;color:#12354b;font-weight:600}.ndg-step__body td{color:#33424f}.ndg-step__body pre{margin:22px 0!important;padding:22px 24px!important;background:#0b1830!important;color:#e6eef5!important;border-radius:14px!important;overflow-x:auto!important;font-family:Consolas,Menlo,\"Liberation Mono\",monospace;font-size:13.5px;line-height:1.55;white-space:pre}.ndg-nav{display:flex;justify-content:space-between;gap:16px;margin:12px 0 8px;flex-wrap:wrap}.ndg-nav a{display:inline-flex;align-items:center;padding:13px 24px;border-radius:10px;background:#f2f2f2;color:#2d799f!important;font-weight:800;font-size:15px;box-shadow:-8px -8px 16px rgba(255,255,255,.92),8px 8px 16px rgba(30,83,116,.10)}.ndg-nav a:hover{color:#1f5f80!important}@media(max-width:820px){.ndg-toc{grid-template-columns:1fr}.ndg-step{grid-template-columns:1fr;gap:16px;padding:26px 22px}.ndg-step__no{width:52px;height:52px;flex:0 0 52px;font-size:22px}.ndg-step__body h2{font-size:22px}}@media(max-width:640px){.ndg{padding:12px 14px}}.ndb-eq{text-align:center;margin:22px 0;overflow-x:auto;font-size:18px}.ndb-card iframe,.ndg-step__body iframe{max-width:100%;border-radius:12px;margin:8px 0}<\/style><div class=\"ndg\"><div class=\"ndg-wrap\"><div class=\"ndg-lead\"><p>Deployment in machine learning is the process of applying a trained model to new data.<\/p>\n<p>Neural Designer can calculate predictions, explain input influence, optimize responses and export models for use in other applications.<\/p>\n<\/div><p class=\"ndg-eyebrow\">Contents<\/p><ul class=\"ndg-toc\"><li><a href=\"#NeuralNetworkOutputs\">Neural network outputs<\/a><\/li><li><a href=\"#OutputData\">Output data<\/a><\/li><li><a href=\"#DirectionalOutputs\">Directional outputs<\/a><\/li><li><a href=\"#SampleInputImportances\">Sample input importances<\/a><\/li><li><a href=\"#ModelInputImportances\">Model input importances<\/a><\/li><li><a href=\"#ResponseOptimization\">Response optimization<\/a><\/li><li><a href=\"#MathematicalExpression\">Mathematical expression<\/a><\/li><li><a href=\"#ProgrammingLanguageExpressions\">Programming language expressions<\/a><\/li><li><a href=\"#DeploymentPackage\">Deployment package<\/a><\/li><\/ul>\n<div class=\"ndg-step\" id=\"NeuralNetworkOutputs\"><div class=\"ndg-step__no\">1<\/div><div class=\"ndg-step__body\"><h2>Neural network outputs<\/h2>\n<p>A neural network produces outputs for every set of inputs.<\/p>\n<p>The input and output formats depend on the model type:<\/p>\n<table><tbody><tr><th>Model<\/th><th>Input<\/th><th>Output<\/th><\/tr><tr><td>Approximation and classification<\/td><td>Variable values<\/td><td>Predictions or probabilities<\/td><\/tr><tr><td>Forecasting<\/td><td>Time series values<\/td><td>Future values<\/td><\/tr><tr><td>Image classification<\/td><td>Image file<\/td><td>Predicted class and probabilities<\/td><\/tr><tr><td>Text classification<\/td><td>Sentence<\/td><td>Predicted class and probabilities<\/td><\/tr><\/tbody><\/table>\n<p>The following table shows the output of a model that estimates a car&#8217;s fuel consumption.<\/p>\n<table><tbody><tr><th>Cylinders<\/th><th>Displacement<\/th><th>Horsepower<\/th><th>Weight<\/th><th>Acceleration<\/th><th>Model year<\/th><th>Fuel consumption<\/th><\/tr><tr><td>8<\/td><td>307<\/td><td>130<\/td><td>3504<\/td><td>12<\/td><td>1980<\/td><td>17 mpg<\/td><\/tr><\/tbody><\/table>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"OutputData\"><div class=\"ndg-step__no\">2<\/div><div class=\"ndg-step__body\"><h2>Output data<\/h2>\n<p>This task applies the model to many new cases and exports their outputs.<\/p>\n<table><tbody><tr><th>Model<\/th><th>Source<\/th><th>Result<\/th><\/tr><tr><td>Numerical and forecasting<\/td><td>Data file<\/td><td>File with predictions<\/td><\/tr><tr><td>Image classification<\/td><td>Image folder<\/td><td>Predictions for every image<\/td><\/tr><tr><td>Text classification<\/td><td>Text file<\/td><td>Prediction for every line<\/td><\/tr><\/tbody><\/table>\n<p>For example, a marketing model can export conversion probabilities for every customer in an input file.<\/p>\n<table><tbody><tr><th>Recency<\/th><th>Frequency<\/th><th>Monetary<\/th><th>Conversion<\/th><\/tr><tr><td>2 months<\/td><td>5 times<\/td><td>125 USD<\/td><td>70%<\/td><\/tr><tr><td>5 months<\/td><td>2 times<\/td><td>20 USD<\/td><td>8%<\/td><\/tr><tr><td>3 months<\/td><td>9 times<\/td><td>225 USD<\/td><td>85%<\/td><\/tr><\/tbody><\/table>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"DirectionalOutputs\"><div class=\"ndg-step__no\">3<\/div><div class=\"ndg-step__body\"><h2>Directional outputs<\/h2>\n<p>Directional outputs show how predictions change when one input varies and all other inputs remain fixed.<\/p>\n<p>They help to understand the model and improve designs or processes.<\/p>\n<p>The following example <a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/yacht-hydrodynamics-modeling\/\">estimates the residuary resistance of a sailing yacht<\/a>. The chart varies the Froude number while keeping the other design variables fixed.<\/p>\n<table><tbody><tr><th>Reference input<\/th><th>Value<\/th><\/tr><tr><td>Center of buoyancy<\/td><td>-2.38<\/td><\/tr><tr><td>Prismatic coefficient<\/td><td>0.56<\/td><\/tr><tr><td>Length displacement<\/td><td>4.79<\/td><\/tr><tr><td>Beam draught ratio<\/td><td>3.94<\/td><\/tr><tr><td>Length beam ratio<\/td><td>3.21<\/td><\/tr><\/tbody><\/table>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/directional_output_example.webp\" alt=\"Directional output example\" \/><\/p>\n<p>The residuary resistance increases rapidly for high Froude numbers, while the remaining inputs stay at the reference point.<\/p>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"SampleInputImportances\"><div class=\"ndg-step__no\">4<\/div><div class=\"ndg-step__body\"><h2>Sample input importances<\/h2>\n<p>Sample input importances explain one prediction by calculating how every output changes with respect to the inputs at a selected sample.<\/p>\n<ul><li>Positive values increase the output.<\/li><li>Negative values decrease the output.<\/li><li>Values close to zero have little local influence.<\/li><\/ul>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"ModelInputImportances\"><div class=\"ndg-step__no\">5<\/div><div class=\"ndg-step__body\"><h2>Model input importances<\/h2>\n<p>Model input importances measure the overall influence of every input on the model outputs.<\/p>\n<p>Neural Designer perturbs up to 50 data samples, accumulates the changes and normalizes the results for comparison.<\/p>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"ResponseOptimization\"><div class=\"ndg-step__no\">6<\/div><div class=\"ndg-step__body\"><h2>Response optimization<\/h2>\n<p>For approximation models, response optimization searches for input values that minimize or maximize selected outputs while satisfying the imposed conditions.<\/p>\n<p>With one objective, it returns the best feasible solution. With several objectives, it returns a Pareto front and highlights an advised trade-off.<\/p>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"MathematicalExpression\"><div class=\"ndg-step__no\">7<\/div><div class=\"ndg-step__body\"><h2>Mathematical expression<\/h2>\n<p>Numerical models can be written as a mathematical function that maps inputs to outputs.<\/p>\n<p>The expression includes scaling, dense layers, activation functions, unscaling and clamping when those operations are present.<\/p>\n<pre>scaled_shear_rate = 2*(shear_rate-50)\/(90-50)-1;\nscaled_particle_diameter = 2*(particle_diameter-0.72)\/(6.596-0.72)-1;\ndense_1_1 = tanh(-1.06007 + 0.448487*scaled_shear_rate - 0.861393*scaled_particle_diameter);\ndense_1_2 = tanh(0.756922 + 2.00716*scaled_shear_rate + 0.391539*scaled_particle_diameter);\nscaled_particles_adhering = -1.30536 - 1.0244*dense_1_1 + 0.56055*dense_1_2;\nparticles_adhering = 0.5*(scaled_particles_adhering+1.0)*(74.75-13.22)+13.22;<\/pre>\n<p>Image and text models do not have a readable closed-form expression. They use a <a href=\"#DeploymentPackage\">deployment package<\/a> instead.<\/p>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"ProgrammingLanguageExpressions\"><div class=\"ndg-step__no\">8<\/div><div class=\"ndg-step__body\"><span id=\"PythonExpression\"><\/span><span id=\"CExpression\"><\/span><h2>Programming language expressions<\/h2>\n<p>Neural Designer exports numerical model expressions to C, Python, JavaScript and PHP.<\/p>\n<p>The generated code reproduces the complete prediction pipeline and can be embedded in another application.<\/p>\n<h3>Python expression<\/h3>\n<pre>def neural_network(inputs):\n    scaled_inputs = scaling_layer(inputs)\n    outputs = dense_layer_0(scaled_inputs)\n    outputs = dense_layer_1(outputs)\n    outputs = unscaling_layer(outputs)\n    return clamping_layer(outputs)<\/pre>\n<h3>C expression<\/h3>\n<pre>void neural_network(const double inputs[], double outputs[])\n{\n    double scaled_inputs[2];\n    double dense_outputs[3];\n    scaling_layer(inputs, scaled_inputs);\n    dense_layer_0(scaled_inputs, dense_outputs);\n    dense_layer_1(dense_outputs, outputs);\n    unscaling_layer(outputs, outputs);\n    clamping_layer(outputs, outputs);\n}<\/pre>\n<\/div><\/div>\n<div class=\"ndg-step\" id=\"DeploymentPackage\"><div class=\"ndg-step__no\">9<\/div><div class=\"ndg-step__body\"><h2>Deployment package<\/h2>\n<p>Image and text classification models can be exported as a complete deployment package.<\/p>\n<p>The package contains everything needed by the model:<\/p>\n<table><tbody><tr><th>Item<\/th><th>Contents<\/th><\/tr><tr><td>Model<\/td><td>Architecture, trained parameters and class labels<\/td><\/tr><tr><td>Engine<\/td><td>Neural Designer Engine and runtime dependencies<\/td><\/tr><tr><td>Wrapper<\/td><td>C program or Python script<\/td><\/tr><tr><td>Documentation<\/td><td>README and command-line examples<\/td><\/tr><\/tbody><\/table>\n<p>The exported folder can be copied to another Windows computer and run without installing Neural Designer or a machine learning framework.<\/p>\n<p>Image packages classify an image or a complete folder. Text packages classify a sentence or every line in a file. Both can return the predicted class, probabilities or percentages.<\/p>\n<p>The Python wrapper requires Python 3. The C wrapper must be compiled once with a C compiler.<\/p>\n<p>Inference uses the CPU by default. GPU execution is available when the bundled engine and runtime support CUDA.<\/p>\n<\/div><\/div>\n<div class=\"ndg-nav\"><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\">\u21d0 Testing Analysis<\/a><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-bibliography\/\">Bibliography \u21d2<\/a><\/div>\n<\/div><\/div>\n","protected":false},"author":122,"featured_media":2702,"template":"","categories":[30],"tags":[36],"class_list":["post-3532","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-tutorials","tag-tutorials"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine learning tutorial: Model deployment<\/title>\n<meta name=\"description\" content=\"Learn different machine learning deployment techniques, from exploring a model to export it to any programming language.\" \/>\n<meta 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