These chapter titles are designed to guide learners from basic DataRobot concepts to advanced techniques and applications.
I. DataRobot Fundamentals (1-20)
- Welcome to DataRobot: Automating Data Science
- Introduction to Automated Machine Learning (AutoML)
- Understanding the DataRobot Platform
- Navigating the DataRobot Interface
- Project Setup: Importing Your Data
- Data Quality Checks and Preprocessing
- Understanding DataRobot's Data Preparation Steps
- Target Selection and Problem Framing
- Choosing the Right Project Type (Regression, Classification, etc.)
- Autopilot Mode: Building Your First Models Automatically
- Understanding DataRobot's Modeling Process
- Model Evaluation Metrics: Accuracy, Precision, Recall, etc.
- Interpreting Model Results and Insights
- Understanding Feature Impact and Importance
- Visualizing Model Performance and Predictions
- Deploying Your Best Model
- Making Predictions with Deployed Models
- Monitoring Model Performance and Accuracy
- Introduction to DataRobot's Visual AI
- Building a Simple Visual AI Model
II. Model Building and Tuning (21-40)
- Exploring DataRobot's Model Repository
- Understanding Different Modeling Algorithms
- Manual Mode: Customizing Your Model Building
- Feature Engineering Techniques in DataRobot
- Advanced Feature Selection Methods
- Tuning Hyperparameters for Optimal Performance
- Understanding Cross-Validation and Holdout
- Working with Partitioning and Sampling
- Building Ensemble Models
- Understanding Blending and Stacking
- Model Comparison and Selection
- Working with Time Series Data
- Forecasting with DataRobot
- Handling Imbalanced Datasets
- Addressing Multicollinearity
- Working with Text Data
- Natural Language Processing (NLP) in DataRobot
- Building NLP Models
- Working with Image Data
- Computer Vision with DataRobot
III. DataRobot Advanced Features (41-60)
- Introduction to DataRobot's MLOps Capabilities
- Model Deployment Options: API, Batch, etc.
- Integrating DataRobot with Other Platforms
- Setting Up DataRobot for Continuous Integration/Continuous Delivery (CI/CD)
- Monitoring Deployed Models in Production
- Model Retraining and Updating
- Understanding Data Drift and Model Decay
- Building Custom Models with DataRobot
- Working with Custom Scoring Metrics
- Understanding DataRobot's Explainable AI (XAI) Features
- Generating Model Explanations and Insights
- Working with Partial Dependence Plots
- Understanding SHAP Values
- Building Rule-Based Models
- Working with Anomaly Detection
- Identifying Outliers and Anomalies in Data
- Time Series Anomaly Detection
- Building a Custom Anomaly Detection Model
- DataRobot's AI Catalog
- Managing and Sharing AI Assets
IV. DataRobot Integrations and APIs (61-80)
- Introduction to the DataRobot API
- Authenticating with the DataRobot API
- Making API Calls and Handling Responses
- Automating DataRobot Tasks with the API
- Integrating DataRobot with Python
- Using the DataRobot Python Client
- Integrating DataRobot with R
- Using the DataRobot R Client
- Connecting DataRobot to Databases
- Integrating DataRobot with Cloud Platforms (AWS, Azure, GCP)
- Deploying Models as REST APIs
- Building Web Applications with DataRobot Models
- Integrating DataRobot with BI Tools
- Creating Dashboards with DataRobot Insights
- Embedding DataRobot Models in Applications
- Building Custom Integrations with DataRobot
- Using Webhooks for Real-Time Notifications
- Managing API Keys and Permissions
- Troubleshooting API Errors
- Building a DataRobot Workflow Automation Script
V. DataRobot Use Cases and Best Practices (81-100)
- DataRobot for Business Intelligence
- DataRobot for Customer Churn Prediction
- DataRobot for Fraud Detection
- DataRobot for Risk Management
- DataRobot for Marketing Optimization
- DataRobot for Supply Chain Optimization
- DataRobot for Healthcare Analytics
- DataRobot for Financial Modeling
- DataRobot for Manufacturing Analytics
- DataRobot for Retail Analytics
- Best Practices for Data Preparation in DataRobot
- Best Practices for Model Selection in DataRobot
- Best Practices for Model Deployment in DataRobot
- Best Practices for Monitoring DataRobot Models
- DataRobot Governance and Compliance
- Building a Data-Driven Culture with DataRobot
- Scaling Data Science with DataRobot
- DataRobot for Citizen Data Scientists
- Advanced DataRobot Project Management
- The Future of AutoML and DataRobot