Mastering DataRobot:

Beginner Level

1. Introduction to DataRobot
   • Objective: Get familiar with the basics of DataRobot and its platform.
   • Key Topics:
     • Overview of DataRobot
     • Navigating the interface
     • Key concepts: projects, datasets, models
     • Introduction to automated machine learning (AutoML)
2. Data Preparation and Exploration
   • Objective: Learn how to prepare and explore data in DataRobot.
   • Key Topics:
     • Importing and exploring datasets
     • Data cleaning and transformation
     • Handling missing data
     • Exploratory data analysis (EDA) using visualizations
3. Building and Evaluating Models
   • Objective: Understand the basics of building and evaluating machine learning models.
   • Key Topics:
     • Creating a new project
     • Selecting target variables and features
     • Building models using AutoML
     • Evaluating model performance using metrics
4. DataRobot Essentials Certification Preparation
   • Objective: Prepare for the DataRobot Essentials certification exam.
   • Key Topics:
     • Core concepts of DataRobot
     • Basic data preparation and model building
     • Best practices for using DataRobot

Intermediate Level

5. Advanced Data Preparation
   • Objective: Master advanced data preparation techniques in DataRobot.
   • Key Topics:
     • Advanced data transformations
     • Feature engineering
     • Data enrichment
     • Handling large datasets
6. Advanced Modeling Techniques
   • Objective: Dive deeper into advanced modeling techniques and customizations.
   • Key Topics:
     • Custom blueprints and recipes
     • Hyperparameter tuning
     • Model blending and stacking
     • Understanding and using unsupervised learning models
7. Deployment and Monitoring
   • Objective: Learn how to deploy and monitor models in production.
   • Key Topics:
     • Model deployment options (real-time, batch, streaming)
     • Setting up prediction environments
     • Monitoring model performance and drift
     • Retraining and updating models
8. DataRobot Advanced Certification Preparation
   • Objective: Prepare for the DataRobot Advanced certification exam.
   • Key Topics:
     • In-depth knowledge of DataRobot features
     • Advanced model building and evaluation
     • Real-world case studies and projects

Advanced Level

9. Custom Modeling and Scripting
   • Objective: Implement custom models and scripts within DataRobot.
   • Key Topics:
     • Using Python and R for custom models
     • DataRobot’s API and custom scripts
     • Integrating external libraries and tools
     • Advanced feature engineering with scripts
10. MLOps and Workflow Automation
    • Objective: Streamline machine learning operations and automate workflows.
    • Key Topics:
      • MLOps best practices
      • Automating workflows with DataRobot
      • Continuous integration and deployment (CI/CD)
      • Collaboration and version control
11. DataRobot Administrator
    • Objective: Manage and administer DataRobot environments.
    • Key Topics:
      • User and project management
      • Security and governance
      • Resource allocation and management
      • Monitoring and troubleshooting
12. Enterprise Data Science and AI Projects
    • Objective: Lead and execute large-scale data science and AI projects.
    • Key Topics:
      • Project planning and management
      • Scaling AI initiatives
      • Ethical AI and responsible AI practices
      • Case studies of enterprise AI implementations

Resources and Practice

• Online Courses and Tutorials:
  • DataRobot University
  • The DataRobot University page on learning paths offers structured educational tracks for various roles and expertise levels. It includes paths for AI and ML practitioners, data scientists, business analysts, and IT professionals. Each path features a combination of courses, certifications, and practical labs designed to build proficiency in DataRobot’s platform and machine learning methodologies. The goal is to enable users to effectively deploy AI solutions in their respective fields. Click here to view the full page. https://learn.datarobot.com/pages/learning-paths
• Books and Documentation:
  • DataRobot User Guide
  • “Practical Data Science” by Andreas François Vermeulen
  • DataRobot official documentation and whitepapers
• Practice and Hands-on Labs:
  • DataRobot Community Edition for hands-on practice
  • Practice projects and case studies
  • Kaggle for datasets and competitions
• Communities and Forums:
  • DataRobot Community Forum
  • Stack Overflow
  • LinkedIn Groups and other professional networks

By following this guided path, you can progress from a beginner to an advanced DataRobot user, equipped with the skills needed to handle data preparation, model building, deployment, and managing large-scale AI projects using DataRobot’s powerful tools and features.