Models Known issues for models. No known issues. Purpose This topic describes the challenges and solutions that models address.Introduction to Production Machine LearningMachine learning (ML) has become one of the most critical capabilities for modern businesses to grow and stay competitive today. From automating internal processes to optimizing the design, creation, and marketing processes behind virtually every product consumed, ML models have permeated almost every aspect of our work and personal lives.Concepts and TerminologyReview basic concepts and terminology related to engines at AWS Account Requirements.Creating and Deploying a Model (QuickStart)Using Cloudera Data Science Workbench, you can create any function within a script and deploy it to a REST API. In a machine learning project, this will typically be a predict function that will accept an input and return a prediction based on the model's parameters.Calling a ModelThis section lists some requirements for model requests and how to test a model using Cloudera Data Science Workbench. Updating Active ModelsA model that is in the Deploying, Deployed, or Stopping stages.Securing Models using Model API KeyYou can prevent unauthorized access to your models by requiring the user to specify a Model API Key in the “Authorization” header of your model HTTP request. This topic covers how to create, test, and use a Model API Key in Cloudera Data Science Workbench.Enabling Model MetricsMetrics are used to track the performance of the models. When you enable model metrics while creating a workspace, the metrics are stored in a scalable metrics store. You can track individual model predictions and analyze metrics using custom code. You can enable the model metrics in CDSW through Cloudera Manager.Tracking Model MetricsUsage GuidelinesThis section calls out some important guidelines you should keep in mind when you start deploying models with Cloudera Data Science Workbench.Known Issues and LimitationsProvides a list of known issues and limitations for deploying Cloudera Data Science Workbench.Model Training and Deployment - Iris DatasetModel Monitoring and AdministrationThis topic describes how to monitor active models and some tasks related to general model administration.Debugging Issues with ModelsThis topic describes some common issues to watch out for during different stages of the model build and deployment process.