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Create machine learning models that run inside FrameworX using Script Classes with ML.NET. The AI writes the C# code, connects it to live tags, creates output tags for predictions, and configures model persistence — all within the FrameworX scripting engine.

Guiding Principle: Always Build Complete

Always generate the full production-ready implementation. Every ML Script Class includes model persistence (SaveModel), startup reload (LoadModel), and the ServerStartup wiring — no stripped-down versions.

One model per interaction. Always create exactly one Script Class ML model per session, targeting one sensor or one prediction goal. Do NOT create multiple ML classes unprompted — even if the solution has many tags. If the user wants additional models, they will ask in follow-up.

Table of Contents

What This Skill Does

Build ML.NET models as FrameworX Script Classes. The AI generates the full C# ML pipeline (data classes, training, prediction, tag integration) based on the user's requirements. Models run server-side, read from input tags, and write predictions to output tags.

Architecture

No Format
Input Tags -> Script Class (ML.NET) -> Output Tags \| \| \| Live data Train / Predict Predictions, scores, from UNS Model persisted to anomaly flags, forecasts solution folder Alarms / Dashboard

When to Use This Skill

Use when:

The user wants to add machine learning to a FrameworX solution
The user mentions "ML", "prediction", "anomaly detection", "forecasting", "regression", "classification", or "ML.NET"
The user mentions industrial goals like "predictive maintenance", "quality control", "fault prediction", "demand forecasting", or "energy prediction"
The user wants to run ML models on-premise inside FrameworX scripts
The user asks "how do I use ML.NET in FrameworX" or "how do I create an ML script"

Do NOT use when:

The user wants cloud-based AI or LLM integration (not ML.NET)
The user only needs simple threshold alarms (no ML needed — use Alarms module)
The user wants to use Python for ML (ML.NET is C# only; Python ML libraries are available for FrameworX, but

...

not covered here)

Prerequisites

Solution open with input tags already created (the tags the model will read from)
Tags should have live or simulated data flowing (Value Simulator or real device)
If starting from scratch, apply the New Solution skill first to create tags and a data source

MCP Tools and Tables

Category	Items
Tools	`get_table_schema`, `write_objects`, `get_objects`, `list_elements`, `search_docs`
Tables	`UnsTags`, `ScriptsClasses`, `ScriptsExpressions`, `ScriptsTasks`

Step 0: Identify the ML Task

Warning
HARD STOP — Do not create any tags, classes, tasks, or expressions until Step 0 is complete. The ML task type and input tags must be confirmed before writing any objects. Proceeding without this information produces incorrect pipelines that are costly to fix.

Before writing any code, the AI must always ask the user the following questions — no exceptions, regardless of how much context is available. Do not silently choose for the user.

Mandatory questions — always ask all three

1. Which ML algorithm do you want to use?
Anomaly Detection — SSA Spike — detects sudden outliers, spikes, or abnormal readings

...

Anomaly Detection — SSA ChangePoint — detects gradual drift or regime shifts

...

Time-Series Forecasting — SSA — predicts future values from historical patterns

...

Regression — FastTree — predicts a continuous value from multiple inputs

...

Binary Classification — FastTree — predicts yes/no outcomes from multiple inputs

...

Not sure

...

which to pick? Describe what you want to achieve and I'll recommend the best fit.

After Q1 is answered, adapt Q2 and Q3 based on the chosen algorithm:

Anomaly Detection — SSA Spike or ChangePoint:

2. Which single tag member should be monitored for anomalies?
(e.g., OilGas_Co/WestTexas_Field/WellPad_A/Well_A01.TubingPressure — full path + member name)
3. The output will be AnomalyScore, IsAnomaly, and LastPrediction tags under <AssetPath>/ML/. Confirm the asset path prefix, or suggest a different output folder.

Time-Series Forecasting — SSA:

2. Which single tag member should be forecast?
(e.g., OilGas_Co/.../Tank_01.Level — full path + member name)
3. How many steps ahead should the forecast horizon be? What does the value represent

...

?

...

Regression — FastTree:

2. Which 2–5 feature tags are the inputs, and which tag is the label (the value to predict)?

...

3. What does the predicted value represent (unit/context

...

)?

Binary Classification

...

— FastTree:

2. Which 2–5 feature tags are the inputs, and which tag is the boolean label

...

?

...

3. What does the yes/no outcome represent?

...

Do not proceed past Step 0 until all three questions are answered.

Goal-to-algorithm mapping (use as suggestions only — always confirm with user)

User Goal	Suggested Algorithm
Predictive maintenance — single sensor	Anomaly Detection (Spike)
Predictive maintenance — multiple sensors	Binary Classification
Detect sensor failures / outliers	Anomaly Detection (Spike)
Detect gradual drift or process shift	Anomaly Detection (ChangePoint)
Predict future values	Time-Series Forecasting (SSA)
Energy / consumption modeling	Regression
Quality control pass/fail	Binary Classification
Fault prediction yes/no	Binary Classification
Production / demand forecasting	Time-Series Forecasting (SSA)
Process output from multiple inputs	Regression

Required information before proceeding

Information	Why
Input tag path(s)	The model reads from these tags
ML algorithm	Determines the ML.NET pipeline to generate
Output semantics	What the predictions mean (anomaly score, forecast value, etc.)

After Step

...

Create tags to receive the model's predictions. Place them under a /ML/ subfolder relative to the input tag's asset path for clean separation.

Code Block
get_table_schema('UnsTags')

Output tag patterns by ML task:

Anomaly Detection outputs

Code Block

{
  "table_type": "UnsTags",
  "data": [
    { "Name": "<AssetPath>/ML/AnomalyScore", "DataType": "Double", "Description": "Anomaly score (0=normal, higher=anomalous)" },
    { "Name": "<AssetPath>/ML/IsAnomaly", "DataType": "Boolean", "Description": "True when anomaly detected" },
    { "Name": "<AssetPath>/ML/LastPrediction", "DataType": "DateTime", "Description": "Timestamp of last prediction" }
  ]
}

Forecasting outputs

Code Block

{
  "table_type": "UnsTags",
  "data": [
    { "Name": "<AssetPath>/ML/Forecast", "DataType": "Double", "Description": "Forecasted value" },
    { "Name": "<AssetPath>/ML/ForecastLower", "DataType": "Double", "Description": "Lower confidence bound" },
    { "Name": "<AssetPath>/ML/ForecastUpper", "DataType": "Double", "Description": "Upper confidence bound" },
    { "Name": "<AssetPath>/ML/LastPrediction", "DataType": "DateTime", "Description": "Timestamp of last prediction" }
  ]
}

Regression outputs

Code Block

{
  "table_type": "UnsTags",
  "data": [
    { "Name": "<AssetPath>/ML/PredictedValue", "DataType": "Double", "Description": "Model predicted value" },
    { "Name": "<AssetPath>/ML/LastPrediction", "DataType": "DateTime", "Description": "Timestamp of last prediction" }
  ]
}

Binary Classification outputs

Code Block

language	c#

{
  "table_type": "UnsTags",
  "data": [
    { "Name": "<AssetPath>/ML/PredictedLabel", "DataType": "Boolean", "Description": "Predicted outcome (true/false)" },
    { "Name": "<AssetPath>/ML/Probability", "DataType": "Double", "Description": "Prediction probability (0-1)" },
    { "Name": "<AssetPath>/ML/LastPrediction", "DataType": "DateTime", "Description": "Timestamp of last prediction" }
  ]
}

Replace <AssetPath> with the actual asset folder path (e.g., Plant/Reactor1).

Step 2: Create the Script Class with ML.NET Enabled

Script Classes using ML.NET require the NamespaceDeclarations field set in write_objects. ML.NET is pre-installed with FrameworX — no NuGet packages or external DLL references are needed.

Warning
Critical pitfall: The field `AddMLNetNamespaces` does not exist in the schema and is silently ignored if passed. Always use `NamespaceDeclarations` with a semicolon-separated string. Omitting it causes `CS0246` / `CS0234` compilation errors with no obvious error message.

The standard ML.NET namespace set to use:

Code Block
"NamespaceDeclarations": "Microsoft.ML;Microsoft.ML.Data;Microsoft.ML.Transforms;Microsoft.ML.Transforms.TimeSeries;Microsoft.ML.Transforms.Text;Microsoft.ML.Trainers;Microsoft.ML.TimeSeries"

Use this exact string for all ML task types. If the class uses VectorType, it is already included via Microsoft.ML.Data.

Code Block
get_table_schema('ScriptsClasses')

Class structure pattern (Complete)

Every ML Script Class follows this structure — always include persistence and LoadModel. No stripped-down versions.

Code Block

language	c#

// 1. Data classes — define input/output schemas for ML.NET
public class SensorData
{
    public float Value { get; set; }
}

public class PredictionResult
{
    // Fields vary by ML task (see task-specific examples below)
}

// 2. Static fields — MLContext and model persist across calls
private static MLContext mlContext = new MLContext(seed: 0);
private static ITransformer model;
private static IDataView lastTrainingDataView;
private static bool modelTrained = false;

// 3. Training buffer — collects data until enough for training
private static List<SensorData> trainingBuffer = new List<SensorData>();
private const int MinTrainingSize = 100;  // adjust per task

// 4. Model path — persisted to solution execution folder
private static readonly string ModelPath = Path.Combine(@Info.GetExecutionPath(), "<ClassName>.mlnet");

// 5. Public entry method — called from Expression or Task
public void Predict(double inputValue)
{
    trainingBuffer.Add(new SensorData { Value = (float)inputValue });

    if (!modelTrained && trainingBuffer.Count >= MinTrainingSize)
        TrainModel();

    if (modelTrained)
        RunPrediction(inputValue);
}

// 6. LoadModel — called from ServerStartup to reload persisted model
public void LoadModel()
{
    if (File.Exists(ModelPath))
    {
        model = mlContext.Model.Load(ModelPath, out _);
        modelTrained = true;
    }
}

// 7. TrainModel — build, fit, and persist the ML pipeline
private void TrainModel()
{
    lastTrainingDataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);
    // pipeline.Fit() call here — see task-specific examples below
    model = pipeline.Fit(lastTrainingDataView);
    modelTrained = true;
    SaveModel();
}

// 8. SaveModel — persist to disk after training
private void SaveModel()
{
    mlContext.Model.Save(model, lastTrainingDataView.Schema, ModelPath);
}

// 9. RunPrediction — transform input and write to output tags
private void RunPrediction(double inputValue) { /* ... */ }

Tag references inside Script Classes

Always use the @Tag. prefix to read or write tag values:

Code Block

// Read from a tag
double temp = @Tag.Plant/Reactor1/Temperature.Value;

// Write to a tag
@Tag.Plant/Reactor1/ML/AnomalyScore.Value = score;
@Tag.Plant/Reactor1/ML/IsAnomaly.Value = true;
@Tag.Plant/Reactor1/ML/LastPrediction.Value = DateTime.Now;

Important: ML.NET expects float but FrameworX tags use double. Always cast with (float) when feeding ML.NET and cast back to double when writing to tags.

Step 3: Write the Class via MCP

Write the complete class with write_objects. The AI generates the full C# code based on the ML task chosen in Step 0.

Code Block

{
  "table_type": "ScriptsClasses",
  "data": [
    {
      "Name": "<ClassName>",
      "Code": "CSharp",
      "Domain": "Server",
      "ClassContent": "Methods",
      "NamespaceDeclarations": "Microsoft.ML;Microsoft.ML.Data;Microsoft.ML.Transforms;Microsoft.ML.Transforms.TimeSeries;Microsoft.ML.Transforms.Text;Microsoft.ML.Trainers;Microsoft.ML.TimeSeries",
      "Contents": "<AI-generated C# code following the class structure pattern>"
    }
  ]
}

Warning
Field names matter: the language field is `Code` (not `Language`), and the code body field is `Contents` (not `Code`). Using wrong field names results in silent data loss.

The AI must generate the Code field dynamically based on:

The ML task type (anomaly, forecasting, regression, classification)
The specific input tag paths
The output tag paths created in Step 1
The appropriate ML.NET pipeline and trainer for the task
Always include SaveModel, LoadModel, and ModelPath — persistence is standard

ML.NET Pipeline Reference by Task

Use these as the basis for generating the Code. Adapt parameters to the user's data characteristics.

Anomaly Detection

The AI chooses the appropriate variant based on the user's description:

SSA Spike Detection — for sudden outliers, spikes, abnormal readings. Use when the user says "detect spikes", "find outliers", "abnormal values", or "sensor failures".
SSA Change Point Detection — for gradual drift, regime shifts, process changes. Use when the user says "detect drift", "process changed", "gradual shift", or "regime change".

Default: SSA Spike Detection (most common industrial use case).

SSA Spike Detection pipeline

Code Block

language	c#

var pipeline = mlContext.Transforms.DetectSpikeBySsa(
    outputColumnName: "Prediction",
    inputColumnName: nameof(SensorData.Value),
    confidence: 95.0,
    pvalueHistoryLength: 10,
    trainingWindowSize: 100,
    seasonalityWindowSize: 10);

Output: double[] Prediction with 0=isAnomaly, 1=score, 2=pValue

SSA Change Point Detection pipeline

Code Block

language	c#

var pipeline = mlContext.Transforms.DetectChangePointBySsa(
    outputColumnName: "Prediction",
    inputColumnName: nameof(SensorData.Value),
    confidence: 95.0,
    changeHistoryLength: 10,
    trainingWindowSize: 100,
    seasonalityWindowSize: 10);

Output: double[] Prediction with 0=alert, 1=score, 2=pValue, 3=martingaleValue

Full class example — Anomaly Detection (Spike)

Code Block

language	c#

public class SensorData
{
    public float Value { get; set; }
}

public class SpikePrediction
{
    [VectorType(3)]
    public double[] Prediction { get; set; }
}

private static MLContext mlContext = new MLContext(seed: 0);
private static ITransformer model;
private static IDataView lastTrainingDataView;
private static bool modelTrained = false;
private static List<SensorData> trainingBuffer = new List<SensorData>();
private const int MinTrainingSize = 100;
private static readonly string ModelPath = Path.Combine(@Info.GetExecutionPath(), "<ClassName>.mlnet");

public void Predict(double inputValue)
{
    trainingBuffer.Add(new SensorData { Value = (float)inputValue });

    if (!modelTrained && trainingBuffer.Count >= MinTrainingSize)
        TrainModel();

    if (modelTrained)
        RunPrediction();
}

public void LoadModel()
{
    if (File.Exists(ModelPath))
    {
        model = mlContext.Model.Load(ModelPath, out _);
        modelTrained = true;
    }
}

private void TrainModel()
{
    lastTrainingDataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);
    var pipeline = mlContext.Transforms.DetectSpikeBySsa(
        outputColumnName: "Prediction",
        inputColumnName: nameof(SensorData.Value),
        confidence: 95.0,
        pvalueHistoryLength: 10,
        trainingWindowSize: 100,
        seasonalityWindowSize: 10);

    model = pipeline.Fit(lastTrainingDataView);
    modelTrained = true;
    SaveModel();
}

private void SaveModel()
{
    mlContext.Model.Save(model, lastTrainingDataView.Schema, ModelPath);
}

private void RunPrediction()
{
    var dataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);
    var transformed = model.Transform(dataView);
    var predictions = mlContext.Data.CreateEnumerable<SpikePrediction>(transformed, reuseRowObject: false).ToList();
    var latest = predictions.Last();

    @Tag.<AssetPath>/ML/IsAnomaly.Value = latest.Prediction[0] == 1;
    @Tag.<AssetPath>/ML/AnomalyScore.Value = latest.Prediction[1];
    @Tag.<AssetPath>/ML/LastPrediction.Value = DateTime.Now;
}

Time-Series Forecasting — SSA

Code Block

language	csharp

var pipeline = mlContext.Forecasting.ForecastBySsa(
    outputColumnName: "ForecastedValues",
    inputColumnName: nameof(SensorData.Value),
    windowSize: 10,
    seriesLength: 100,
    trainSize: trainingBuffer.Count,
    horizon: 5,
    confidenceLevel: 0.95f,
    confidenceLowerBoundColumn: "LowerBound",
    confidenceUpperBoundColumn: "UpperBound");

Output: float[] ForecastedValues, float[] LowerBound, float[] UpperBound

Full class example — Time-Series Forecasting

Code Block

language	csharp

public class SensorData
{
    public float Value { get; set; }
}

public class ForecastOutput
{
    public float[] ForecastedValues { get; set; }
    public float[] LowerBound { get; set; }
    public float[] UpperBound { get; set; }
}

private static MLContext mlContext = new MLContext(seed: 0);
private static TimeSeriesPredictionEngine<SensorData, ForecastOutput> forecastEngine;
private static ITransformer model;
private static IDataView lastTrainingDataView;
private static bool modelTrained = false;
private static List<SensorData> trainingBuffer = new List<SensorData>();
private const int MinTrainingSize = 100;
private static readonly string ModelPath = Path.Combine(@Info.GetExecutionPath(), "<ClassName>.mlnet");

public void Predict(double inputValue)
{
    trainingBuffer.Add(new SensorData { Value = (float)inputValue });

    if (!modelTrained && trainingBuffer.Count >= MinTrainingSize)
        TrainModel();

    if (modelTrained)
        RunPrediction();
}

public void LoadModel()
{
    if (File.Exists(ModelPath))
    {
        model = mlContext.Model.Load(ModelPath, out _);
        forecastEngine = model.CreateTimeSeriesEngine<SensorData, ForecastOutput>(mlContext);
        modelTrained = true;
    }
}

private void TrainModel()
{
    lastTrainingDataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);
    var pipeline = mlContext.Forecasting.ForecastBySsa(
        outputColumnName: "ForecastedValues",
        inputColumnName: nameof(SensorData.Value),
        windowSize: 10,
        seriesLength: 100,
        trainSize: trainingBuffer.Count,
        horizon: 5,
        confidenceLevel: 0.95f,
        confidenceLowerBoundColumn: "LowerBound",
        confidenceUpperBoundColumn: "UpperBound");

    model = pipeline.Fit(lastTrainingDataView);
    forecastEngine = model.CreateTimeSeriesEngine<SensorData, ForecastOutput>(mlContext);
    modelTrained = true;
    SaveModel();
}

private void SaveModel()
{
    mlContext.Model.Save(model, lastTrainingDataView.Schema, ModelPath);
}

private void RunPrediction()
{
    var forecast = forecastEngine.Predict();
    @Tag.<AssetPath>/ML/Forecast.Value = (double)forecast.ForecastedValues[0];
    @Tag.<AssetPath>/ML/ForecastLower.Value = (double)forecast.LowerBound[0];
    @Tag.<AssetPath>/ML/ForecastUpper.Value = (double)forecast.UpperBound[0];
    @Tag.<AssetPath>/ML/LastPrediction.Value = DateTime.Now;
}

Regression — FastTree

Code Block

language	csharp

var pipeline = mlContext.Transforms.Concatenate("Features", "Feature1", "Feature2", "Feature3")
    .Append(mlContext.Regression.Trainers.FastTree(
        labelColumnName: "Label",
        featureColumnName: "Features",
        numberOfLeaves: 20,
        numberOfTrees: 100,
        minimumExampleCountPerLeaf: 10,
        learningRate: 0.2));

Output: float Score (predicted continuous value)

Full class example — Regression

Code Block

language	csharp

public class ProcessData
{
    public float Feature1 { get; set; }  // e.g., Temperature
    public float Feature2 { get; set; }  // e.g., Pressure
    public float Feature3 { get; set; }  // e.g., Flow
    public float Label { get; set; }     // e.g., EnergyConsumption (what we predict)
}

public class RegressionPrediction
{
    public float Score { get; set; }
}

private static MLContext mlContext = new MLContext(seed: 0);
private static ITransformer model;
private static IDataView lastTrainingDataView;
private static PredictionEngine<ProcessData, RegressionPrediction> predictionEngine;
private static bool modelTrained = false;
private static List<ProcessData> trainingBuffer = new List<ProcessData>();
private const int MinTrainingSize = 200;
private static readonly string ModelPath = Path.Combine(@Info.GetExecutionPath(), "<ClassName>.mlnet");

public void Predict(double input1, double input2, double input3, double label)
{
    trainingBuffer.Add(new ProcessData
    {
        Feature1 = (float)input1,
        Feature2 = (float)input2,
        Feature3 = (float)input3,
        Label = (float)label
    });

    if (!modelTrained && trainingBuffer.Count >= MinTrainingSize)
        TrainModel();

    if (modelTrained)
        RunPrediction(input1, input2, input3);
}

public void LoadModel()
{
    if (File.Exists(ModelPath))
    {
        model = mlContext.Model.Load(ModelPath, out _);
        predictionEngine = mlContext.Model.CreatePredictionEngine<ProcessData, RegressionPrediction>(model);
        modelTrained = true;
    }
}

private void TrainModel()
{
    lastTrainingDataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);
    var pipeline = mlContext.Transforms.Concatenate("Features",
            nameof(ProcessData.Feature1),
            nameof(ProcessData.Feature2),
            nameof(ProcessData.Feature3))
        .Append(mlContext.Regression.Trainers.FastTree(
            labelColumnName: "Label",
            featureColumnName: "Features",
            numberOfLeaves: 20,
            numberOfTrees: 100,
            minimumExampleCountPerLeaf: 10,
            learningRate: 0.2));

    model = pipeline.Fit(lastTrainingDataView);
    predictionEngine = mlContext.Model.CreatePredictionEngine<ProcessData, RegressionPrediction>(model);
    modelTrained = true;
    SaveModel();
}

private void SaveModel()
{
    mlContext.Model.Save(model, lastTrainingDataView.Schema, ModelPath);
}

private void RunPrediction(double input1, double input2, double input3)
{
    var input = new ProcessData
    {
        Feature1 = (float)input1,
        Feature2 = (float)input2,
        Feature3 = (float)input3
    };
    var result = predictionEngine.Predict(input);

    @Tag.<AssetPath>/ML/PredictedValue.Value = (double)result.Score;
    @Tag.<AssetPath>/ML/LastPrediction.Value = DateTime.Now;
}

Note on Regression training data: The Predict() method above accepts a label parameter during the training phase. This is the known actual value that the model learns to predict. During prediction-only mode (after training), the label is not needed. The AI should adapt the method signature based on whether the user has a label tag or wants to train from historical data.

Binary Classification — FastTree

Code Block

language	csharp

var pipeline = mlContext.Transforms.Concatenate("Features", "Feature1", "Feature2", "Feature3")
    .Append(mlContext.BinaryClassification.Trainers.FastTree(
        labelColumnName: "Label",
        featureColumnName: "Features"));

Output: bool PredictedLabel, float Score, float Probability

Full class example — Binary Classification

Code Block

public class ProcessData
{
    public float Feature1 { get; set; }  // e.g., Vibration
    public float Feature2 { get; set; }  // e.g., Temperature
    public float Feature3 { get; set; }  // e.g., Current
    public bool Label { get; set; }      // e.g., DidFault (true/false)
}

public class ClassificationPrediction
{
    public bool PredictedLabel { get; set; }
    public float Score { get; set; }
    public float Probability { get; set; }
}

private static MLContext mlContext = new MLContext(seed: 0);
private static ITransformer model;
private static IDataView lastTrainingDataView;
private static PredictionEngine<ProcessData, ClassificationPrediction> predictionEngine;
private static bool modelTrained = false;
private static List<ProcessData> trainingBuffer = new List<ProcessData>();
private const int MinTrainingSize = 200;
private static readonly string ModelPath = Path.Combine(@Info.GetExecutionPath(), "<ClassName>.mlnet");

public void Predict(double input1, double input2, double input3, bool label)
{
    trainingBuffer.Add(new ProcessData
    {
        Feature1 = (float)input1,
        Feature2 = (float)input2,
        Feature3 = (float)input3,
        Label = label
    });

    if (!modelTrained && trainingBuffer.Count >= MinTrainingSize)
        TrainModel();

    if (modelTrained)
        RunPrediction(input1, input2, input3);
}

public void LoadModel()
{
    if (File.Exists(ModelPath))
    {
        model = mlContext.Model.Load(ModelPath, out _);
        predictionEngine = mlContext.Model.CreatePredictionEngine<ProcessData, ClassificationPrediction>(model);
        modelTrained = true;
    }
}

private void TrainModel()
{
    lastTrainingDataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);
    var pipeline = mlContext.Transforms.Concatenate("Features",
            nameof(ProcessData.Feature1),
            nameof(ProcessData.Feature2),
            nameof(ProcessData.Feature3))
        .Append(mlContext.BinaryClassification.Trainers.FastTree(
            labelColumnName: "Label",
            featureColumnName: "Features"));

    model = pipeline.Fit(lastTrainingDataView);
    predictionEngine = mlContext.Model.CreatePredictionEngine<ProcessData, ClassificationPrediction>(model);
    modelTrained = true;
    SaveModel();
}

private void SaveModel()
{
    mlContext.Model.Save(model, lastTrainingDataView.Schema, ModelPath);
}

private void RunPrediction(double input1, double input2, double input3)
{
    var input = new ProcessData
    {
        Feature1 = (float)input1,
        Feature2 = (float)input2,
        Feature3 = (float)input3
    };
    var result = predictionEngine.Predict(input);

    @Tag.<AssetPath>/ML/PredictedLabel.Value = result.PredictedLabel;
    @Tag.<AssetPath>/ML/Probability.Value = (double)result.Probability;
    @Tag.<AssetPath>/ML/LastPrediction.Value = DateTime.Now;
}

Note on Classification training data: Same as Regression — the label parameter is needed during training. The AI should adapt based on whether a label tag exists. For fault prediction, the label is typically a boolean tag that records when a fault occurred historically.

Step 4: Create the Trigger (Expression or Task)

Connect the ML class to live tag changes so the model runs automatically.

Option A: Expression (OnChange) — for single-input models

Best for anomaly detection and forecasting on a single tag.

Code Block
get_table_schema('ScriptsExpressions')

Code Block

{
  "table_type": "ScriptsExpressions",
  "data": [
    {
      "Name": "ML_Predict_<SensorName>",
      "ObjectName": "",
      "Expression": "@Script.Class.<ClassName>.Predict(@Tag.<AssetPath>.<Member>)",
      "Execution": "OnChange",
      "Trigger": "<AssetPath>"
    }
  ]
}

Warning

ObjectName must be empty when the ML class writes prediction results to output tags internally (inside RunPrediction()). Setting a non-empty ObjectName on a void method call causes a type assignment error.

Use Trigger (not TriggerTag) — TriggerTag is not a valid field and is silently ignored, causing the expression to never fire. Trigger accepts the tag path without the Tag. prefix.

Option B: Task (Periodic) — for multi-input models

Best for regression and classification where multiple tags feed the model simultaneously.

Code Block
get_table_schema('ScriptsTasks')

Code Block

{
  "table_type": "ScriptsTasks",
  "data": [
    {
      "Name": "ML_Predict_Periodic",
      "Language": "CSharp",
      "Execution": "Periodic",
      "Period": 5000,
      "Code": "@Script.Class.<ClassName>.Predict(\n    @Tag.Plant/Reactor1/Temperature.Value,\n    @Tag.Plant/Reactor1/Pressure.Value,\n    @Tag.Plant/Reactor1/Flow.Value,\n    @Tag.Plant/Reactor1/EnergyConsumption.Value);"
    }
  ]
}

Warning
The `@` prefix is mandatory when referencing runtime objects inside ScriptsTasks. `Script.Class.<Name>.Predict(...)` without `@` causes `CS0234: The type or namespace name 'Class' does not exist in the namespace 'Script'`. Always use `@Script.Class.<Name>.Predict(...)` inside ScriptsTasks — the `@` prefix is required for all runtime object references.

Option C: Startup model reload — always include

Always wire LoadModel() into ServerStartup. Read the existing task first (document object — read-modify-write), then append the LoadModel call.

Code Block

language	csharp

Script.Class.<ClassName>.LoadModel();

Read the existing ServerStartup task first (document object — read-modify-write), then append the LoadModel call.

Step 5: Verify

After creating all objects:

Confirm the solution is set to Multiplatform before starting the runtime. ML.NET requires .NET 8+ — if the solution was created as Windows (.NET 4.8), training will fail with a System.Math / CpuMath error. Instruct the user:
"Before starting the runtime, please confirm your solution is set to Multiplatform: Solution → Settings → Target Platform = Multiplatform, then Product → Modify."
Only proceed if the user confirms this is already set.
Do NOT start the runtime automatically. Inform the user that all scripts are configured and that they can start the runtime whenever ready. Only call designer_action('start_runtime') if the user explicitly requests it.
Wait for training — the model needs MinTrainingSize data points before predictions begin
Check output tags — verify LastPrediction timestamp is updating
Screenshot — if a dashboard exists, take a screenshot to confirm predictions are flowing

Common Pitfalls

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Mistake

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Why It Happens

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How to Avoid

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Missing ML.NET namespaces

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Used AddMLNetNamespaces: true (field does not exist — silently ignored) or omitted NamespaceDeclarations entirely

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Always set NamespaceDeclarations: "Microsoft.ML;Microsoft.ML.Data;..." in write_objects. AddMLNetNamespaces is not a valid field.

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CS0234 error in ScriptsTasks

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Called Script.Class.<Name>.Predict(...) without @ prefix

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Always use @Script.Class.<Name>.Predict(...) inside ScriptsTasks — the @ prefix is required for all runtime object references.

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Tag reference without @Tag.

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Confusing with Expression syntax

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Always @Tag.Path/Name.Value inside Script Classes

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Model lost on restart

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SaveModel or LoadModel not wired up

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Always include SaveModel() after TrainModel() and wire LoadModel() in ServerStartup

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Training on every call

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No guard for already-trained model

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Use modelTrained boolean flag

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Wrong data types

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ML.NET expects float, tags are double

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Cast with (float) when feeding ML.NET, cast back to double when writing tags

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Expression ObjectName missing Tag. prefix

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Confusing tag path vs expression binding

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Expression ObjectName needs Tag. prefix; the UNS tag itself does not

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Non-empty ObjectName on void ML Predict() call

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Expression tries to assign void return to a tag

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Leave ObjectName empty when the ML class writes to output tags internally inside RunPrediction()

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Used TriggerTag field in Expression

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Field does not exist — silently ignored, expression never fires

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Use Trigger field with the tag path (no Tag. prefix)

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Class is document object

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Partial write replaces entire class

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Always read-modify-write for existing classes

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CS0029 bool-to-int on Digital tag write

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Digital tags are backed by int, not bool — assigning a C# bool expression causes a type error

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Never assign a raw bool to a Digital tag. Use a ternary: @Tag.Path/ML/Flag.Value = (condition) ? 1 : 0; — never = boolVar; or = (bool expression);

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CS0246 Script Class not found in Tasks

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Tasks and Classes compile in the same pass; if the Class isn't ordered first, Tasks that reference it fail

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Set BuildOrder: "1" on every ML Script Class. Tasks that call it do not need an explicit BuildOrder — they default to a later pass.

0: Load the Specific Sub-Skill

Once the algorithm is chosen, load and follow the corresponding sub-skill for complete implementation steps (output tags, Script Class code, trigger, verification, pitfalls):

Algorithm	Sub-Skill to Load
Anomaly Detection — SSA Spike or ChangePoint	`search_docs(fetch_url='https://docs.tatsoft.com/display/FX/Skill+ML.NET+Anomaly+Detection')`
Time-Series Forecasting — SSA	`search_docs(fetch_url='https://docs.tatsoft.com/display/FX/Skill+ML.NET+Forecasting')`
Regression — FastTree	`search_docs(fetch_url='https://docs.tatsoft.com/display/FX/Skill+ML.NET+Regression')`
Binary Classification — FastTree	`search_docs(fetch_url='https://docs.tatsoft.com/display/FX/Skill+ML.NET+Classification')`

Each sub-skill is fully self-contained with output tags, complete C# class examples, MCP write commands, trigger configuration, and pitfalls specific to that ML task.

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Could not load type 'System.Math' from assembly 'System.Runtime' at training time

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Solution is targeting .NET 4.8 (Windows platform) — ML.NET CpuMath trainers (FastTree, SSA) are incompatible with .NET 4.8

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Decision Guide

Scenario	ML Task	Trigger	Notes
Single sensor, detect outliers/spikes	Anomaly Detection (Spike)	Expression OnChange	Fast, one tag in / flags out
Single sensor, detect gradual drift	Anomaly Detection (ChangePoint)	Expression OnChange

...

For "drift" or "regime change"
Single sensor, predict future values	Forecasting (SSA)	Expression OnChange or Periodic	Outputs forecast + confidence bounds
Multiple sensors → one continuous value	Regression	Task Periodic	Energy prediction, process modeling
Multiple sensors → yes/no	Binary Classification	Task Periodic	Fault prediction, quality pass/fail
User says "predictive maintenance" + single sensor	Anomaly Detection	Expression OnChange	Most common PdM entry point
User says "predictive maintenance" + multiple sensors	Binary Classification	Task Periodic	Predicts failure from combined inputs
User says "quality control"	Binary Classification	Task Periodic	Pass/fail prediction
User says "forecast" or "predict demand"	Forecasting (SSA)	Expression OnChange or Periodic	Time-series based
User says "you decide" + single sensor	Anomaly Detection	Expression OnChange	Safest default for monitoring
User says "you decide" + multiple sensors	Regression	Task Periodic	Most general multi-input approach

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Key

Guiding Principle: Always Build Complete

When to Use This Skill

Prerequisites

MCP Tools and Tables

Step 0: Identify the ML Task

Mandatory questions — always ask all three

Goal-to-algorithm mapping (use as suggestions only — always confirm with user)

Required information before proceeding

After Step

Anomaly Detection outputs

Forecasting outputs

Regression outputs

Binary Classification outputs

Step 2: Create the Script Class with ML.NET Enabled

Class structure pattern (Complete)

Tag references inside Script Classes

Step 3: Write the Class via MCP

ML.NET Pipeline Reference by Task

Anomaly Detection

SSA Spike Detection pipeline

SSA Change Point Detection pipeline

Full class example — Anomaly Detection (Spike)

Time-Series Forecasting — SSA

Full class example — Time-Series Forecasting

Regression — FastTree

Full class example — Regression

Binary Classification — FastTree

Full class example — Binary Classification

Step 4: Create the Trigger (Expression or Task)

Option A: Expression (OnChange) — for single-input models

Option B: Task (Periodic) — for multi-input models

Option C: Startup model reload — always include

Step 5: Verify

Common Pitfalls

0: Load the Specific Sub-Skill

Decision Guide