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  • What's New
  • Getting Started
    • Dataiku DSS - The Value Proposition
    • +Dataiku DSS - Project Walkthrough
      • The NY Taxi Project through the AI Lifecycle
      • The AI Lifecycle: Data Acquisition
      • The AI Lifecycle: Data Exploration
      • The AI Lifecycle: Data Preparation
      • The AI Lifecycle: Experiment
      • The AI Lifecycle: Deploy
      • The AI Lifecycle: Orchestrate
    • Business Analyst Quick Start
    • AI Consumer Quick Start
    • Data Scientist Quick Start
    • Data Engineer Quick Start
    • +From Excel To Dataiku DSS
      • Introduction
      • Data Cleaning
      • Using Formulas
      • Working with Dates
      • Removing Duplicates
      • Filtering Rows
      • Sampling Rows
      • Split a Dataset
      • Append Datasets
      • Joining Datasets
      • Aggregate and Pivot
      • Sorting Values
      • Top Values
  • Setup and Administration
    • Concept Summary: Connections to SQL Databases
    • Remapping Connections in a DSS Instance
    • Working with MongoDB in DSS
    • Integration with Amazon Redshift
    • How to Leverage Compute Resource Usage Data
  • Data Preparation
    • Concept: Recipes in DSS
    • +Preparing Data with Visual Recipes
      • Concept: Distinct Recipe
      • Concept: Group Recipe
      • Concept: Join Recipe
      • Concept: Pivot Recipe
      • The Pivot Recipe
      • Reshaping Data from Long to Wide Format
      • Creating Excel-Style Pivot Tables with the Pivot Recipe
      • Concept: Prepare Recipe
      • Concept: Date Handling in DSS
      • Concept: Formulas in DSS
      • Advanced Prepare Recipe Usage
      • How to reorder or hide the columns of a dataset
      • Concept: Filter Recipe
      • Hands On: Fuzzy Join Recipe
      • Concept: Sample Recipe
      • Concept: Sort Recipe
      • Concept: Split Recipe
      • Concept: Stack Recipe
      • Concept: Top N Recipe
      • Concept: Window Recipe
      • Visual Window Analytic Functions
      • Concept Summary: Architecture Model for Databases
      • How to segment your data using statistical quantiles
    • +Preparing Data with Code Recipes
      • Concept Summary: SQL Recipe
      • Using PySpark in DSS
      • Using SparkR in DSS
    • +Preparing Data with Plugin Recipes
      • Events Aggregator (Plugin)
    • +Building Data Pipelines
      • Data Pipelines
      • Concept: Computation Engine
      • Concept: Jobs
      • Build Datasets
      • Where does it all happen?
      • How to enable SQL pipelines in the Flow
    • Repartitioning a non-partitioned dataset
  • Exploring Datasets
    • +Connecting to and Exploring Data
      • Concept: Datasets in DSS
      • Concept: Partitioning
      • Concept: Connections
      • Concept: Schema
      • Concept: Storage Type
      • Concept: Meaning
      • Concept: Sampling
      • Concept: Analyze
      • Where can I see how many records are in my entire dataset?
      • Utilizing MS Access in Dataiku DSS
    • +Charts
      • Concept: Charts
      • Concept Summary: In-Database Charts
      • Paneled and Animated Charts
      • How to display non-aggregated metrics in charts
      • How to sort on a measure that is not displayed in charts?
    • +Exploring Data in the Lab
      • Concept: The Lab
      • Concept Summary: SQL Notebooks
  • Reporting & Insights
    • +Dashboards
      • Concept: Dashboards
      • Cannot display a web content insight in a dashboard
      • Hands-On Tutorial: What-If Analysis With Interactive Scoring
    • +R Markdown
      • Concept: R Markdown Reports
      • R Markdown Reports in Dataiku DSS
    • +Webapps in Dataiku DSS
      • Hands-On: Dash Webapp
      • Hands-On: Bokeh Webapp
      • Hands-On: Shiny Webapp
      • Hands-On: Standard Webapp
      • Tutorial: Create an HTML/JavaScript Webapp to Draw the San Francisco Crime Map
      • Use Custom Static Files (Javascript, CSS) in a Webapp
      • How to Adapt a D3.js Template in a Webapp
      • Use a React Frontend to Create a Webapp
      • How-To: Display an Image With Bokeh
      • Upload to Dataiku DSS in a Webapp
      • Download from a Dataiku DSS Webapp
    • Concept: Visualization Plugins
  • Managing Your Work & Collaboration
    • Concept: Homepage
    • Concept: Project
    • Concept: Collaboration
    • Concept: Flow
    • How to copy a recipe in your Flow
    • Navigating Dataiku DSS with the right panel
    • Flow Zones
    • Tags
    • Using Wikis to Share Knowledge
    • How-To: Export a Wiki to PDF
    • Using Discussions to Communicate with Teammates
    • Git for Projects
    • +Flow Views & Actions
      • Flow Views: Zones, Tags, & More
      • Hands-On Tutorial: Flow Zones, Tags, & More Flow Views
      • Concept: Schema Propagation & Consistency Checks
      • Concept: Connection Changes & Flow Item Reuse
      • Concept: Dataset Building Strategies
      • Hands-On Tutorial: Perform Flow Actions
    • Best Practices for Collaborating in Dataiku DSS
    • Best Practices to Improve Your Productivity
  • Analytics and Machine Learning
    • +Interactive Visual Statistics
      • Concept: Statistics Worksheet
      • Concept: Statistics Card
      • Concept: Categorical and Numerical Variables
      • Concept: Factor and Response
      • Concept: Fit Curves and Distributions
      • Concept: Correlation Matrix
      • Concept: Principal Component Analysis (PCA)
      • Concept: Hypothesis Testing
      • Concept: Test Categories
      • Concept: Grouping Variable
      • Concept: Adjustment Method
      • Hands-On: Interactive Visual Statistics
    • +Intro to Machine Learning
      • Concept Summary: Introduction to Machine Learning
      • Concept Summary: Predictive Modeling
      • Concept Summary: Model Validation
      • Concept: Model Evaluation
      • Concept Summary: Regression Algorithms
      • Concept Summary: Classification Algorithms
      • Concept Summary: Clustering Algorithms
      • K-Means
      • Hierarchical Clustering
      • What’s next
    • +Visual Machine Learning
      • Machine Learning Basics
      • Interpreting Regression Models’ Outputs
      • How to identify clusters and name them
      • Deploy and Score a Model
      • Concept: Model Lifecycle Management
      • Concept Summary: Partitioned Models
      • Hands-On: Partitioned Models
      • How do I train a stratified or partitioned model?
      • Custom Models in Visual ML
      • Using MLLib in the Dataiku DSS interface
      • Why don’t the values in the Visual ML chart match the final scores for each algorithm?
      • In Visual ML, why am I getting the error “All values of the target are equal,” when they are not?
      • Compute a subpopulation analysis for white-box ML
    • Monitoring model drift with Dataiku DSS
    • +Time Series
      • Time Series Basics
      • Time Series Preparation
      • Time Series Modeling and Forecasting
      • How Dataiku DSS Handles and Displays Date & Time
    • Introduction to Deep Learning with Code
    • +Natural Language Processing (NLP)
      • Concept: Introduction to Natural Language Processing
      • Hands-On: Getting Started with NLP
      • Concept: The Challenges of Natural Language Processing (NLP)
      • Hands-On: Cleaning Text Data
      • Concept: Handling Text Features for ML
      • Hands-On: Handling Text Features for ML
      • Sentiment Analysis in Dataiku DSS (Plugin)
      • Recognize authors style using the Gutenberg plugin
      • Natural Language Processing with Code
      • How to Use the Python Natural Language Toolkit (NLTK) in Dataiku
      • How to use spaCy models in Dataiku DSS
    • +Image Classification with Visual Tools
      • Hands-On: Create Your Project and Prepare the Data
      • Hands-On: Install the Deep Learning Plugins
      • Concept Summary: Pre-Trained Models
      • Hands-On: Add a Pre-Trained Model to the Flow
      • Classify a Set of Test Images with the Pre-Trained Model
      • Hands-On: Transfer Learning to Retrain the Model
      • Hands-On: Analyze and Understand Your Model with Tensorboard
      • Hands-On: Object Detection
      • Wrap Up
    • Image Classification with Code
    • +Geospatial Analytics
      • Creating Maps in Dataiku DSS without Code
      • Geographic Processing with Dataiku DSS
      • Working with Shapefiles and US Census Data in DSS
    • +Active Learning
      • Active Learning for classification problems
      • Active Learning for object detection problems
      • Help on Active Learning Webapp
      • Active Learning for object detection problems using Dataiku Apps
      • Active Learning for tabular data classification problems using Dataiku Apps
    • +Reinforcement Learning
      • Introduction to Reinforcement Learning
      • Q-Learning
      • Deep Q-Learning
  • Advanced Code
    • +Python and Dataiku DSS
      • Python in Dataiku DSS
      • Reading or writing a dataset with custom Python code
      • How to use SQL from a Python Recipe in DSS
      • Sessionization in SQL, Hive, Python, and Pig
      • Custom Python Models
      • Tuning XGBoost Models in Python
      • How to add a group to a Dataiku DSS Project using a Python Script
      • How to set a timeout for a particular scenario build step via a custom Python step?
      • How to use Azure AutoML from a Dataiku DSS Notebook
      • How to enable auto-completion in Jupyter Notebook
      • Concept: Managed Folders
      • Hands-On Tutorial: Managed Folders
    • +R and Dataiku DSS
      • Basics of R in Dataiku DSS
      • Hands-On Tutorial: Dataiku DSS for R Users (Advanced)
      • Mining Association Rules and Frequent Item Sets with R and Dataiku DSS
      • Upgrading the R version used in Dataiku DSS
    • +Work Environment
      • Using Jupyter Notebooks in DSS
      • How to Edit Dataiku Recipes and Plugins in Visual Studio Code
      • How to Edit Dataiku Recipes and Plugins in PyCharm
      • How to Edit Dataiku Recipes and Plugins in Sublime
      • How to Edit Dataiku Recipes in RStudio
      • Setting a Code Environment
      • Cloning a Library from a Remote Git Repository
      • How-To: Import a Notebook from GitHub
      • Dataiku DSS Memory Optimization tips: Backend, Python/R, Spark jobs
    • +Dataiku DSS APIs
      • Concept: APIs in Dataiku DSS
      • Concept: The dataiku Package
      • Concept: The Public API
      • Hands-On Tutorial: The Public API in Dataiku DSS
      • Concept: APIs outside Dataiku DSS
  • Operationalization
    • -Automation
      • Concept: Metrics & Checks
      • Concept: Scenarios
      • Concept: Custom Metrics, Checks & Scenarios
      • Reporting Scenario Activities
      • Model Lifecycle
      • Automation Quick Start
      • Hands-On: Automation with Metrics, Checks & Scenarios
      • How to Create a Google Chat Reporter
      • How to programmatically set email recipients in a “Send email” reporter using the API?
      • How to create a Jira issue automatically upon a DSS scenario execution failure
      • Can I control which datasets in my Flow get rebuilt during a scenario?
      • How to build missing partitions with a scenario
    • Hands-On Tutorial: Deploying a Flow to Production
    • Hands-On Tutorial: Deploying to Real-Time Scoring
    • Deploying Multiple Models to the API Node for A/B Testing
    • +Dataiku Applications
      • An Introduction to Dataiku Applications
      • Create a Visual Application
      • Create an Application-As-Recipe
      • Difference Between Webapps and Dataiku Applications
      • Dataiku Applications: Use Cases
    • +Building CI/CD pipelines for Dataiku DSS
      • Building a Jenkins pipeline for API services in Dataiku DSS
      • Building a Jenkins pipeline for Dataiku DSS with Project Deployer
      • Building an Azure Pipeline for Dataiku DSS with Project Deployer
      • Building a Jenkins pipeline for Dataiku DSS without Project Deployer
    • +Variables
      • Variables in Flows, Webapps, and Dataiku Applications
      • A Look at Coding with Variables
      • Concept Summary: Defining Variables
      • Concept Summary: Using Variables in a Code Recipe
      • Concept Summary: Modifying the Value of Variables
      • Hands-On: Variables for Coders
  • Plugin Development & Management
    • +Plugin Management
      • Plugins in Dataiku DSS
      • Plugin Store Usage
      • Getting Started with the Dataiku DSS Plugin Store
      • Sharing a Plugin as a Zip Archive
      • Hands-On Tutorial: Plugin Store
      • Managing Plugin Versions with Git
      • Cloning a Plugin from a Remote Git Repository
    • +Examples of Plugin Component Development
      • How to Create a Custom Recipe
      • How to Create a Custom Dataset
      • How to Create a Partitioned Custom Dataset
      • How to Create a Custom Webapp
      • How to Create a Custom Machine Learning Algorithm
      • Setting Up Your Code Editor to Develop Dataiku Plugins
      • Plugin Naming Policies and Conventions
      • What’s Next
  • Governance
    • Concept: Catalog and Global Search
    • Using global search in Dataiku DSS
    • Data Governance with the GDPR Plugin
    • How to use project folders in Dataiku DSS
    • Why can’t I drag and drop a folder into Dataiku DSS?
    • How to duplicate a Dataiku DSS project
    • How to find out which users are logged onto the Dataiku DSS instance
    • Which activities in Dataiku DSS require that a user be added to the allowed_user_groups local Unix group?
  • Use Cases
    • Airport Traffic by US and International Carriers
    • Predictive Maintenance
    • Churn Prediction
    • Web Logs Analysis
    • Network Optimization
    • Bike Sharing Usage Patterns
    • Crawl budget prediction for enhanced SEO with the OnCrawl plugin
    • A/B Testing for Event Promotion
  • Industry Solutions
    • Distribution Spatial Footprint
    • RFM-Enriched Customer Lifetime Value
    • Market Basket Analysis
    • News Sentiment Stock Alert System
    • Interactive Document Intelligence for ESG
    • Real Estate Pricing
    • Optimizing Omnichannel Marketing in Pharma
    • Drug Repurposing through Graph Analytics
  • Dataiku Online
    • How to begin a Dataiku Online free trial
    • Starting a Dataiku Online Trial from Snowflake Partner Connect
    • Manage Dataiku Online from the Launchpad
    • How to Connect to Your Data on Dataiku Online
    • How to invite users to your Dataiku Online space
    • How to Add Plugins to Your Dataiku Online Space
    • Work With Python on Dataiku Online
 
Dataiku Academy
You are viewing the Knowledge Base for version 9.0 of DSS.
  • Docs »
  • Operationalization »
  • Automation

Automation¶

Learn how to use metrics, checks, and scenarios to schedule jobs and monitor the status and quality of your dataset.

Tip

To validate your knowledge of this area, register for the the Automation course, part of the Advanced Designer learning path, on the Dataiku Academy.

Articles¶

  • Concept: Metrics & Checks
  • Concept: Scenarios
  • Concept: Custom Metrics, Checks & Scenarios
  • Reporting Scenario Activities
  • Model Lifecycle
  • Automation Quick Start
  • Hands-On: Automation with Metrics, Checks & Scenarios
  • How to Create a Google Chat Reporter
  • How to programmatically set email recipients in a “Send email” reporter using the API?
  • How to create a Jira issue automatically upon a DSS scenario execution failure
  • Can I control which datasets in my Flow get rebuilt during a scenario?
  • How to build missing partitions with a scenario
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