Notebooks

• Deploying SKLearn Model
  • Requirements
  • 1. Initialize MLP Resources
    • 1.1 Set MLP Server
    • 1.2 Set Active Project
    • 1.3 Set Active Model
  • 2. Train and Deploy
    • 2.1 Train and Upload Model
  • 2.2 Deploy Model
  • 2.3 Send Test Request
  • 3.4 Delete Deployment
• Deploying XGBoost Model
  • Requirements
  • 1. Initialize Merlin Resources
    • 1.1 Set Merlin Server
    • 1.2 Set Active Project
    • 1.3 Set Active Model
  • 2. Train Model And Deploy
    • 2.1 Create Model Version and Upload Model
    • 2.2 Deploy Model
    • 2.3 Send Test Request
    • 2.4 Delete Deployment
• Deploying Tensorflow Model
  • Requirements
  • 1. Initialize Merlin Resources
    • 1.1 Set Merlin Server
    • 1.2 Set Active Project
    • 1.3 Set Active Model
  • 2. Train Model
    • 2.1 Prepare Train and Test Set
    • 2.2 Create Input Function
    • 2.3 Define Feature Columns
    • 2.4 Build Estimators
    • 2.5 Train Estimator
    • 2.6 Serialize Model
  • 3. Upload and Deploy Model
    • 3.1 Deploy Model
    • 3.2 Send Test Request
    • 3.3 Delete Deployment
• Deploying PyTorch Model
  • Requirements
  • 1. Initialize
    • 1.1 Set Merlin Server
    • 1.2 Set Active Project
    • 1.3 Set Active Model
  • 2. Train Model
    • 2.1 Prepare training data
    • 2.2 Create PyTorch Model
    • 2.3 Train and Check Prediction
  • 3. Deploy Model
    • 3.1 Serialize Model
    • 3.2 Create Model Version and Upload
    • 3.3 Deploy Model
    • 3.4 Send Test Request
    • 3.5 Delete Deployment
• Deploying Python Function Model
  • Requirements
  • 1. Initialize
    • 1.1 Set Server
    • 1.2 Set Active Project
    • 1.3 Set Active Model
  • 2. Train Model
    • 2.1 Train First Model
    • 2.2 Train Second Model
    • 2.3 Create PyFunc Model
  • 3. Deploy Model
    • 3.1 Create Model Version and Upload
    • 3.2 Deploy Model
    • 3.3 Send Test Request
    • 3.4 Delete Deployment
• Custom Metrics
  • Requirements
  • 1. Initialize
    • 1.1 Set Server
    • 1.2 Set Active Project
    • 1.3 Set Active Model
  • 2. Create Model
    • 2.1 Define PyFunc Model Class
  • 3. Deploy Model
    • 3.1 Create Model Version and Upload
    • 3.2 Deploy Model
    • 3.3 Send Test Request
    • 3.4 Delete Deployment
• Batch Prediction
  • Requirements
  • 1. Train Model
  • 2. Wrap Model
    • IMPORTANT
  • 3. Upload To Merlin
    • 3.1 Initialization
    • 3.2 Set Active Project
    • 3.3 Set Active Model
    • 3.4 Create New Model Version And Upload
  • 4. Create Batch Prediction Job
    • 4.1 Configuring BQ Source
    • 4.2 Configuring BQ Sink
    • 4.3 Configuring Job
    • 4.4 Start Batch Prediction Job
• Predicting NY Taxi Fare Using Batch Prediction
  • Requirements
  • Problem Statement
  • 1. Train Model
  • 2. Wrap Model
    • IMPORTANT
  • 3. Upload To Merlin
    • 3.1 Initialization
    • 3.2 Set Active Project
    • 3.3 Set Active Model
    • 3.4 Create New Model Version And Upload
  • 4. Create Batch Prediction Job