VBAF.ML.ModelServer.ps1
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#Requires -Version 5.1 <# .SYNOPSIS Model Server - Serve VBAF models via HTTP REST API .DESCRIPTION Implements model serving from scratch using .NET HttpListener. Designed as a TEACHING resource - every step explained. Features included: - REST API wrapper : serve any VBAF model over HTTP - Batch prediction : send multiple inputs, get multiple outputs - Real-time inference : single prediction with latency tracking - Model monitoring : log predictions, latency, errors - A/B testing : split traffic between two model versions No external dependencies - uses built-in .NET HttpListener! .NOTES Part of VBAF - Phase 7 Production Features - v2.1.0 PS 5.1 compatible Teaching project - MLOps serving concepts explained! ENDPOINTS (default port 8080): GET /health - server health check GET /info - model info and stats POST /predict - single prediction POST /predict/batch - batch predictions GET /metrics - monitoring metrics GET /ab/stats - A/B test statistics #> # ============================================================ # TEACHING NOTE: What is model serving? # Training is offline - you train once, save the model. # Serving is online - you expose the model as an API so # applications can call it in real time! # # REST API pattern: # Client sends: POST /predict {"features": [120, 3, 5]} # Server returns: {"prediction": 245.3, "latency_ms": 2.1} # # Key concepts: # Latency : how fast is each prediction? (ms) # Throughput : how many predictions per second? # Monitoring : are predictions drifting? Any errors? # A/B testing: which model version performs better in prod? # ============================================================ # ============================================================ # SERVER STATE (script-scope so it persists) # ============================================================ $script:ServerRunning = $false $script:ServerJob = $null $script:MonitoringLog = [System.Collections.ArrayList]::new() $script:ABLog = [System.Collections.ArrayList]::new() # ============================================================ # PREDICTION ENGINE # ============================================================ # TEACHING NOTE: The prediction engine wraps any VBAF model. # It handles the conversion from JSON input -> model input -> JSON output. # ============================================================ function Invoke-VBAFPrediction { param( [object] $Model, [string] $ModelType, [double[]] $Features, [object] $Scaler = $null ) $start = [System.Diagnostics.Stopwatch]::StartNew() # Scale features if scaler provided $input = $Features if ($null -ne $Scaler) { $input = $Scaler.Transform(@(,$Features)) $input = $input[0] } $prediction = switch ($ModelType) { "LinearRegression" { $Model.Predict(@(,$input))[0] } "RidgeRegression" { $Model.Predict(@(,$input))[0] } "LassoRegression" { $Model.Predict(@(,$input))[0] } "LogisticRegression"{ $Model.Predict(@(,$input))[0] } "DecisionTree" { $Model.Predict(@(,$input))[0] } "RandomForest" { $Model.Predict(@(,$input))[0] } "GaussianNaiveBayes"{ $Model.Predict(@(,$input))[0] } "KMeans" { $Model.Predict(@(,$input))[0] } default { $Model.Predict(@(,$input))[0] } } $start.Stop() return @{ Prediction = $prediction LatencyMs = [Math]::Round($start.Elapsed.TotalMilliseconds, 3) } } function Invoke-VBAFBatchPrediction { param( [object] $Model, [string] $ModelType, [double[][]] $FeatureMatrix, [object] $Scaler = $null ) $start = [System.Diagnostics.Stopwatch]::StartNew() $results = @() foreach ($features in $FeatureMatrix) { $input = $features if ($null -ne $Scaler) { $scaled = $Scaler.Transform(@(,([double[]]$features))) $input = [double[]]$scaled[0] } $pred = $Model.Predict(@(,$input))[0] $results += $pred } $start.Stop() return @{ Predictions = $results Count = $results.Length LatencyMs = [Math]::Round($start.Elapsed.TotalMilliseconds, 3) AvgLatencyMs= [Math]::Round($start.Elapsed.TotalMilliseconds / [Math]::Max(1,$results.Length), 3) } } # ============================================================ # MONITORING # ============================================================ # TEACHING NOTE: Monitoring in production means tracking: # - Prediction distribution: is the model returning unusual values? # - Latency: is inference getting slower over time? # - Error rate: how many requests are failing? # - Input drift: are incoming features different from training data? # ============================================================ function Write-VBAFMonitoringLog { param( [string] $ModelName, [string] $ModelVersion, [string] $Endpoint, [double] $LatencyMs, [object] $Prediction, [bool] $IsError = $false ) $entry = @{ Timestamp = (Get-Date).ToString("yyyy-MM-dd HH:mm:ss.fff") ModelName = $ModelName ModelVersion = $ModelVersion Endpoint = $Endpoint LatencyMs = $LatencyMs Prediction = $Prediction IsError = $IsError } $script:MonitoringLog.Add($entry) | Out-Null } function Get-VBAFMonitoringStats { param([string]$ModelName = "", [int]$LastN = 100) $log = $script:MonitoringLog if ($ModelName -ne "") { $log = $log | Where-Object { $_.ModelName -eq $ModelName } } if ($log.Count -eq 0) { Write-Host "📊 No monitoring data yet" -ForegroundColor Yellow return } $recent = @($log | Select-Object -Last $LastN) $errors = @($recent | Where-Object { $_.IsError }).Count $latencies = $recent | ForEach-Object { $_.LatencyMs } $avgLat = [Math]::Round(($latencies | Measure-Object -Average).Average, 2) $maxLat = [Math]::Round(($latencies | Measure-Object -Maximum).Maximum, 2) $preds = $recent | Where-Object { -not $_.IsError } | ForEach-Object { [double]$_.Prediction } Write-Host "" Write-Host "📊 Monitoring Stats" -ForegroundColor Green if ($ModelName -ne "") { Write-Host (" Model : {0}" -f $ModelName) -ForegroundColor Cyan } Write-Host (" Requests : {0} (last {1})" -f $recent.Count, $LastN) -ForegroundColor White Write-Host (" Errors : {0} ({1:F1}%)" -f $errors, (100.0*$errors/$recent.Count)) -ForegroundColor $(if ($errors -gt 0) {"Red"} else {"Green"}) Write-Host (" Latency : avg={0}ms max={1}ms" -f $avgLat, $maxLat) -ForegroundColor White if ($preds.Count -gt 0) { $avgPred = [Math]::Round(($preds | Measure-Object -Average).Average, 4) $minPred = [Math]::Round(($preds | Measure-Object -Minimum).Minimum, 4) $maxPred = [Math]::Round(($preds | Measure-Object -Maximum).Maximum, 4) Write-Host (" Preds : avg={0} min={1} max={2}" -f $avgPred, $minPred, $maxPred) -ForegroundColor White } Write-Host "" } function Export-VBAFMonitoringLog { param([string]$Path = ".\vbaf_monitoring.csv") if ($script:MonitoringLog.Count -eq 0) { Write-Host "No monitoring data to export" -ForegroundColor Yellow return } $lines = @("Timestamp,ModelName,ModelVersion,Endpoint,LatencyMs,Prediction,IsError") foreach ($e in $script:MonitoringLog) { $lines += '"{0}","{1}","{2}","{3}",{4},{5},{6}' -f ` $e.Timestamp, $e.ModelName, $e.ModelVersion, $e.Endpoint, $e.LatencyMs, $e.Prediction, $e.IsError } $lines | Set-Content $Path -Encoding UTF8 Write-Host ("📄 Monitoring log exported: {0} ({1} rows)" -f $Path, $script:MonitoringLog.Count) -ForegroundColor Green } # ============================================================ # A/B TESTING FRAMEWORK # ============================================================ # TEACHING NOTE: A/B testing in ML means: # - Model A (control) : current production model # - Model B (treatment) : new candidate model # - Split traffic 50/50 (or configurable) # - Compare predictions and latency # - Decide which model to promote to 100% traffic # # Example: HousePricePredictor v1.0 vs v1.1 # After 1000 requests: v1.1 has lower RMSE -> promote! # ============================================================ $script:ABConfig = $null function Start-VBAFABTest { param( [string] $TestName, [object] $ModelA, [string] $ModelAName, [string] $ModelAVersion, [string] $ModelAType, [object] $ModelB, [string] $ModelBName, [string] $ModelBVersion, [string] $ModelBType, [double] $TrafficSplitA = 0.5, # fraction sent to A (0.5 = 50/50) [object] $ScalerA = $null, [object] $ScalerB = $null ) $script:ABConfig = @{ TestName = $TestName Started = (Get-Date).ToString("yyyy-MM-dd HH:mm:ss") ModelA = $ModelA ModelAName = $ModelAName ModelAVersion = $ModelAVersion ModelAType = $ModelAType ModelB = $ModelB ModelBName = $ModelBName ModelBVersion = $ModelBVersion ModelBType = $ModelBType TrafficSplitA = $TrafficSplitA ScalerA = $ScalerA ScalerB = $ScalerB Rng = [System.Random]::new(42) StatsA = @{ Count=0; TotalLatency=0.0; Predictions=@() } StatsB = @{ Count=0; TotalLatency=0.0; Predictions=@() } } $script:ABLog.Clear() Write-Host "" Write-Host ("🧪 A/B Test started: {0}" -f $TestName) -ForegroundColor Green Write-Host (" Model A : {0} v{1} ({2:P0} traffic)" -f $ModelAName, $ModelAVersion, $TrafficSplitA) -ForegroundColor Cyan Write-Host (" Model B : {0} v{1} ({2:P0} traffic)" -f $ModelBName, $ModelBVersion, (1-$TrafficSplitA)) -ForegroundColor Yellow Write-Host "" } function Invoke-VBAFABPredict { param([double[]]$Features) if ($null -eq $script:ABConfig) { Write-Host "❌ No A/B test running. Call Start-VBAFABTest first." -ForegroundColor Red return $null } $cfg = $script:ABConfig $useA = $cfg.Rng.NextDouble() -lt $cfg.TrafficSplitA $model = if ($useA) { $cfg.ModelA } else { $cfg.ModelB } $type = if ($useA) { $cfg.ModelAType } else { $cfg.ModelBType } $scaler= if ($useA) { $cfg.ScalerA } else { $cfg.ScalerB } $name = if ($useA) { "A" } else { "B" } $result = Invoke-VBAFPrediction -Model $model -ModelType $type -Features $Features -Scaler $scaler # Log $stats = if ($useA) { $cfg.StatsA } else { $cfg.StatsB } $stats.Count++ $stats.TotalLatency += $result.LatencyMs $stats.Predictions += $result.Prediction $logEntry = @{ Timestamp = (Get-Date).ToString("yyyy-MM-dd HH:mm:ss.fff") Model = $name Prediction = $result.Prediction LatencyMs = $result.LatencyMs } $script:ABLog.Add($logEntry) | Out-Null return @{ Model = $name Prediction = $result.Prediction LatencyMs = $result.LatencyMs } } function Get-VBAFABStats { if ($null -eq $script:ABConfig) { Write-Host "❌ No A/B test running" -ForegroundColor Red return } $cfg = $script:ABConfig $sA = $cfg.StatsA $sB = $cfg.StatsB Write-Host "" Write-Host ("🧪 A/B Test Results: {0}" -f $cfg.TestName) -ForegroundColor Green Write-Host (" Started : {0}" -f $cfg.Started) -ForegroundColor DarkGray Write-Host "" Write-Host (" {0,-12} {1,8} {2,12} {3,12} {4,12}" -f "Model","Requests","Avg Latency","Avg Pred","StdDev Pred") -ForegroundColor Yellow Write-Host (" {0}" -f ("-" * 60)) -ForegroundColor DarkGray foreach ($nm in @("A","B")) { $s = if ($nm -eq "A") { $sA } else { $sB } $mName = if ($nm -eq "A") { "$($cfg.ModelAName) v$($cfg.ModelAVersion)" } else { "$($cfg.ModelBName) v$($cfg.ModelBVersion)" } if ($s.Count -eq 0) { Write-Host (" {0,-12} {1,8}" -f "Model $nm", 0) -ForegroundColor DarkGray continue } $avgLat = [Math]::Round($s.TotalLatency / $s.Count, 3) $preds = $s.Predictions $avgPred = [Math]::Round(($preds | Measure-Object -Average).Average, 4) $mean = ($preds | Measure-Object -Average).Average $stdDev = [Math]::Round([Math]::Sqrt((($preds | ForEach-Object { ($_ - $mean)*($_ - $mean) } | Measure-Object -Sum).Sum / $preds.Count)), 4) $color = if ($nm -eq "A") { "Cyan" } else { "Yellow" } $shortName = if ($mName.Length -gt 12) { $mName.Substring(0,12) } else { $mName } Write-Host (" {0,-12} {1,8} {2,11}ms {3,12} {4,12}" -f "Model $nm ($shortName)", $s.Count, $avgLat, $avgPred, $stdDev) -ForegroundColor $color } # Recommendation Write-Host "" if ($sA.Count -gt 0 -and $sB.Count -gt 0) { $latA = $sA.TotalLatency / $sA.Count $latB = $sB.TotalLatency / $sB.Count $winner = if ($latB -lt $latA) { "B ($($cfg.ModelBName) v$($cfg.ModelBVersion))" } else { "A ($($cfg.ModelAName) v$($cfg.ModelAVersion))" } Write-Host (" 💡 Faster model: {0}" -f $winner) -ForegroundColor Green } Write-Host "" } function Stop-VBAFABTest { if ($null -eq $script:ABConfig) { Write-Host "No A/B test running" -ForegroundColor Yellow; return } $name = $script:ABConfig.TestName Get-VBAFABStats $script:ABConfig = $null Write-Host ("🛑 A/B Test stopped: {0}" -f $name) -ForegroundColor Yellow } # ============================================================ # HTTP SERVER (runs in background job) # ============================================================ # TEACHING NOTE: HttpListener is a built-in .NET class that # listens for HTTP requests on a port. It's not as fast as # nginx or IIS, but it works in pure PS 5.1! # # Request flow: # 1. Client sends HTTP POST to http://localhost:8080/predict # 2. HttpListener receives the request # 3. We parse the JSON body # 4. Call the model's Predict method # 5. Return JSON response with prediction + latency # ============================================================ function Start-VBAFModelServer { param( [object] $Model, [string] $ModelName, [string] $ModelVersion = "1.0.0", [string] $ModelType, [object] $Scaler = $null, [int] $Port = 8080, [string] $Prefix = "" ) if ($script:ServerRunning) { Write-Host "⚠️ Server already running. Call Stop-VBAFModelServer first." -ForegroundColor Yellow return } $urlPrefix = if ($Prefix -ne "") { $Prefix } else { "http://localhost:$Port/" } # Serialize model for the background job $modelJson = @{ ModelName = $ModelName ModelVersion = $ModelVersion ModelType = $ModelType Port = $Port UrlPrefix = $urlPrefix } | ConvertTo-Json Write-Host "" Write-Host ("🚀 Starting VBAF Model Server...") -ForegroundColor Green Write-Host (" Model : {0} v{1}" -f $ModelName, $ModelVersion) -ForegroundColor Cyan Write-Host (" Type : {0}" -f $ModelType) -ForegroundColor Cyan Write-Host (" URL : {0}" -f $urlPrefix) -ForegroundColor Yellow Write-Host "" Write-Host " Endpoints:" -ForegroundColor White Write-Host (" GET {0}health - health check" -f $urlPrefix) -ForegroundColor DarkGray Write-Host (" GET {0}info - model info" -f $urlPrefix) -ForegroundColor DarkGray Write-Host (" POST {0}predict - single prediction" -f $urlPrefix) -ForegroundColor DarkGray Write-Host (" POST {0}predict/batch - batch predictions" -f $urlPrefix) -ForegroundColor DarkGray Write-Host (" GET {0}metrics - monitoring metrics" -f $urlPrefix) -ForegroundColor DarkGray Write-Host "" # Store model reference in script scope for the sync server $script:ServedModel = $Model $script:ServedModelName = $ModelName $script:ServedModelVersion = $ModelVersion $script:ServedModelType = $ModelType $script:ServedScaler = $Scaler $script:ServedPort = $Port $script:ServedPrefix = $urlPrefix $script:ServerRunning = $true $script:RequestCount = 0 Write-Host "✅ Server ready! Press Ctrl+C or call Stop-VBAFModelServer to stop." -ForegroundColor Green Write-Host " (Server runs synchronously - open a new PS window to send requests)" -ForegroundColor DarkGray Write-Host "" # Start synchronous listener $listener = [System.Net.HttpListener]::new() $listener.Prefixes.Add($urlPrefix) try { $listener.Start() Write-Host "🟢 Listening on $urlPrefix" -ForegroundColor Green while ($script:ServerRunning) { # Non-blocking check with timeout $contextTask = $listener.GetContextAsync() $waited = 0 while (-not $contextTask.IsCompleted -and $script:ServerRunning) { Start-Sleep -Milliseconds 100 $waited += 100 if ($waited -ge 30000) { break } # 30s timeout, loop again } if (-not $contextTask.IsCompleted) { continue } $context = $contextTask.Result $request = $context.Request $response = $context.Response $script:RequestCount++ $method = $request.HttpMethod $path = $request.Url.AbsolutePath.ToLower().TrimEnd('/') $body = "" if ($request.HasEntityBody) { $reader = [System.IO.StreamReader]::new($request.InputStream) $body = $reader.ReadToEnd() $reader.Close() } $responseBody = "" $statusCode = 200 try { if ($path -eq "/health" -or $path -eq "") { $responseBody = (@{ status = "healthy" model = $script:ServedModelName version = $script:ServedModelVersion requests= $script:RequestCount uptime = (Get-Date).ToString("yyyy-MM-dd HH:mm:ss") } | ConvertTo-Json) } elseif ($path -eq "/info") { $responseBody = (@{ model = $script:ServedModelName version = $script:ServedModelVersion type = $script:ServedModelType port = $script:ServedPort endpoints = @("/health","/info","/predict","/predict/batch","/metrics") } | ConvertTo-Json) } elseif ($path -eq "/predict" -and $method -eq "POST") { $payload = $body | ConvertFrom-Json $features = [double[]]$payload.features $result = Invoke-VBAFPrediction -Model $script:ServedModel -ModelType $script:ServedModelType ` -Features $features -Scaler $script:ServedScaler Write-VBAFMonitoringLog -ModelName $script:ServedModelName -ModelVersion $script:ServedModelVersion ` -Endpoint "/predict" -LatencyMs $result.LatencyMs -Prediction $result.Prediction $responseBody = (@{ model = $script:ServedModelName version = $script:ServedModelVersion prediction = $result.Prediction latency_ms = $result.LatencyMs } | ConvertTo-Json) } elseif ($path -eq "/predict/batch" -and $method -eq "POST") { $payload = $body | ConvertFrom-Json $matrix = @() foreach ($row in $payload.features) { $matrix += ,[double[]]$row } $result = Invoke-VBAFBatchPrediction -Model $script:ServedModel -ModelType $script:ServedModelType ` -FeatureMatrix $matrix -Scaler $script:ServedScaler $responseBody = (@{ model = $script:ServedModelName version = $script:ServedModelVersion predictions = $result.Predictions count = $result.Count latency_ms = $result.LatencyMs avg_latency_ms = $result.AvgLatencyMs } | ConvertTo-Json) } elseif ($path -eq "/metrics") { $log = $script:MonitoringLog | Where-Object { $_.ModelName -eq $script:ServedModelName } $errors = @($log | Where-Object { $_.IsError }).Count $lats = @($log | ForEach-Object { $_.LatencyMs }) $avgLat = if ($lats.Count -gt 0) { ($lats | Measure-Object -Average).Average } else { 0 } $responseBody = (@{ model = $script:ServedModelName total_requests= $script:RequestCount logged_preds = $log.Count error_count = $errors avg_latency_ms= [Math]::Round($avgLat, 3) } | ConvertTo-Json) } else { $statusCode = 404 $responseBody = (@{ error="Not found"; path=$path } | ConvertTo-Json) } } catch { $statusCode = 500 $responseBody = (@{ error=$_.Exception.Message } | ConvertTo-Json) Write-VBAFMonitoringLog -ModelName $script:ServedModelName -ModelVersion $script:ServedModelVersion ` -Endpoint $path -LatencyMs 0 -Prediction $null -IsError $true } $bytes = [System.Text.Encoding]::UTF8.GetBytes($responseBody) $response.StatusCode = $statusCode $response.ContentType = "application/json" $response.ContentLength64 = $bytes.Length $response.OutputStream.Write($bytes, 0, $bytes.Length) $response.OutputStream.Close() Write-Host ("{0} {1,-6} {2,-25} {3}" -f (Get-Date).ToString("HH:mm:ss"), $method, $path, $statusCode) -ForegroundColor DarkGray } } finally { $listener.Stop() $script:ServerRunning = $false Write-Host "🔴 Server stopped" -ForegroundColor Yellow } } function Stop-VBAFModelServer { $script:ServerRunning = $false Write-Host "🛑 Stop signal sent" -ForegroundColor Yellow } # ============================================================ # CLIENT HELPERS (call the server from PowerShell) # ============================================================ function Invoke-VBAFServerPredict { param( [double[]] $Features, [int] $Port = 8080 ) $body = @{ features=$Features } | ConvertTo-Json $result = Invoke-RestMethod -Uri "http://localhost:$Port/predict" -Method POST ` -Body $body -ContentType "application/json" return $result } function Invoke-VBAFServerBatchPredict { param( [double[][]] $FeatureMatrix, [int] $Port = 8080 ) $body = @{ features=$FeatureMatrix } | ConvertTo-Json -Depth 5 $result = Invoke-RestMethod -Uri "http://localhost:$Port/predict/batch" -Method POST ` -Body $body -ContentType "application/json" return $result } function Get-VBAFServerHealth { param([int]$Port = 8080) return Invoke-RestMethod -Uri "http://localhost:$Port/health" -Method GET } function Get-VBAFServerMetrics { param([int]$Port = 8080) return Invoke-RestMethod -Uri "http://localhost:$Port/metrics" -Method GET } # ============================================================ # TEST # 1. Run VBAF.LoadAll.ps1 # # --- Train a model --- # 2. $data = Get-VBAFDataset -Name "HousePrice" # $scaler = [StandardScaler]::new() # $Xs = $scaler.FitTransform($data.X) # $model = [LinearRegression]::new() # $model.Fit($Xs, $data.y) # # --- Single prediction (no server) --- # 3. $result = Invoke-VBAFPrediction -Model $model -ModelType "LinearRegression" ` # -Features @(120.0, 3.0, 5.0) -Scaler $scaler # Write-Host "Prediction: $($result.Prediction) Latency: $($result.LatencyMs)ms" # # --- Batch prediction --- # 4. $batch = Invoke-VBAFBatchPrediction -Model $model -ModelType "LinearRegression" ` # -FeatureMatrix @(@(120.0,3.0,5.0),@(80.0,2.0,10.0),@(200.0,4.0,2.0)) ` # -Scaler $scaler # Write-Host "Predictions: $($batch.Predictions -join ', ')" # Write-Host "Total: $($batch.LatencyMs)ms Per-item: $($batch.AvgLatencyMs)ms" # # --- A/B Test --- # 5. $model2 = [RidgeRegression]::new(0.1) # $model2.Fit($Xs, $data.y) # Start-VBAFABTest -TestName "LinearVsRidge" ` # -ModelA $model -ModelAName "HousePricePredictor" -ModelAVersion "1.0.0" -ModelAType "LinearRegression" -ScalerA $scaler ` # -ModelB $model2 -ModelBName "HousePricePredictor" -ModelBVersion "1.1.0" -ModelBType "RidgeRegression" -ScalerB $scaler # # Simulate 20 requests # for ($i=0; $i -lt 20; $i++) { # $feat = @([double](80+$i*5), [double](2+$i%3), [double](1+$i%10)) # Invoke-VBAFABPredict -Features $feat | Out-Null # } # Get-VBAFABStats # Stop-VBAFABTest # # --- Monitoring --- # 6. Get-VBAFMonitoringStats # Export-VBAFMonitoringLog -Path "C:\Temp\vbaf_log.csv" # # --- HTTP Server (open a regular PowerShell console to test) --- # Note: Only one ISE can run at a time. Start server in ISE, then open # a regular PowerShell console (not ISE) for Window 2 commands. # 7. Start-VBAFModelServer -Model $model -ModelName "HousePricePredictor" ` # -ModelVersion "1.0.0" -ModelType "LinearRegression" -Scaler $scaler -Port 8080 # # In another PS window: # # Invoke-VBAFServerPredict -Features @(120.0, 3.0, 5.0) # # Get-VBAFServerHealth # ============================================================ Write-Host "📦 VBAF.ML.ModelServer.ps1 loaded [v2.1.0 🏭]" -ForegroundColor Green Write-Host " Functions : Invoke-VBAFPrediction" -ForegroundColor Cyan Write-Host " Invoke-VBAFBatchPrediction" -ForegroundColor Cyan Write-Host " Start-VBAFModelServer" -ForegroundColor Cyan Write-Host " Stop-VBAFModelServer" -ForegroundColor Cyan Write-Host " Start-VBAFABTest" -ForegroundColor Cyan Write-Host " Invoke-VBAFABPredict" -ForegroundColor Cyan Write-Host " Get-VBAFABStats" -ForegroundColor Cyan Write-Host " Stop-VBAFABTest" -ForegroundColor Cyan Write-Host " Get-VBAFMonitoringStats" -ForegroundColor Cyan Write-Host " Export-VBAFMonitoringLog" -ForegroundColor Cyan Write-Host " Invoke-VBAFServerPredict" -ForegroundColor Cyan Write-Host " Get-VBAFServerHealth" -ForegroundColor Cyan Write-Host "" Write-Host " Quick start:" -ForegroundColor Yellow Write-Host ' $r = Invoke-VBAFPrediction -Model $model -ModelType "LinearRegression" -Features @(120.0,3.0,5.0) -Scaler $scaler' -ForegroundColor White Write-Host ' Write-Host "Prediction: $($r.Prediction) Latency: $($r.LatencyMs)ms"' -ForegroundColor White Write-Host ' Start-VBAFABTest -TestName "v1vsv2" -ModelA $m1 -ModelAName "HP" -ModelAVersion "1.0" -ModelAType "LinearRegression" -ModelB $m2 -ModelBName "HP" -ModelBVersion "1.1" -ModelBType "RidgeRegression"' -ForegroundColor White Write-Host "" |