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Agile Machine Learning with DataRobot

Agile Machine Learning with DataRobot

Bipin Chadha, Sylvester Juwe

Kup pozycję na ebookpoint.pl

DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization.

You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities.

By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors.

  • Agile Machine Learning with DataRobot
  • Contributors
  • About the authors
  • About the reviewer
  • Preface
    • Who this book is for
    • What this book covers
    • To get the most out of this book
    • Code in Action
    • Download the color images
    • Conventions used
    • Get in touch
    • Share Your Thoughts
  • Section 1: Foundations
  • Chapter 1: What Is DataRobot and Why You Need It?
    • Technical requirements
    • Data science processes for generating business value
      • Problem understanding
      • Data preparation
      • Model development
      • Model deployment
      • Model maintenance
    • Challenges associated with data science
    • DataRobot architecture
      • Hosting platform
      • Data sources
      • Core functions
      • External interactions
      • Users
    • Navigating and using DataRobot features
    • Addressing data science challenges with DataRobot
      • Lack of good-quality data
      • Explosion of data
      • Shortage of experienced data scientists
      • Immature tools and environments
      • Black box models
      • Bias and fairness
    • Summary
  • Chapter 2: Machine Learning Basics
    • Data preparation
      • Supervised learning dataset
      • Time series datasets
      • Data cleansing
      • Data normalization and standardization
      • Outliers
      • Missing values
      • Category encoding
      • Consolidate categories
      • Target leakage
      • Term-document matrix
      • Data transformations
      • Collinearity checks
      • Data partitioning
    • Data visualization
    • Machine learning algorithms
      • Unsupervised learning
      • Reinforcement learning
      • Ensemble/blended models
      • Blueprints
    • Performance metrics
    • Understanding the results
      • Lift chart
      • Confusion matrix (binary and multiclass)
      • ROC
      • Accuracy over time
      • Feature impacts
      • Feature Fit
      • Feature Effects
      • Prediction Explanations
      • Shapley values
    • Summary
  • Chapter 3: Understanding and Defining Business Problems
    • Understanding the system context
    • Understanding the why and the how
      • Process diagrams
      • Interaction diagrams
      • State diagrams
      • Causal diagrams
    • Getting to the root of the business problem
    • Defining the ML problem
    • Determining predictions, actions, and consequences for Responsible AI
    • Operationalizing and generating value
    • Summary
    • Further reading
  • Section 2: Full ML Life Cycle with DataRobot: Concept to Value
  • Chapter 4: Preparing Data for DataRobot
    • Technical requirements
      • Automobile Dataset
      • Appliances Energy Prediction Dataset
    • Connecting to data sources
    • Aggregating data for modeling
    • Cleansing the dataset
    • Working with different types of data
    • Engineering features for modeling
    • Summary
  • Chapter 5: Exploratory Data Analysis with DataRobot
    • Data ingestion and data cataloging
    • Data quality assessment
    • EDA
    • Setting the target feature and correlation analysis
    • Feature selection
    • Summary
  • Chapter 6: Model Building with DataRobot
    • Configuring a modeling project
    • Building models and the model leaderboard
    • Understanding model blueprints
    • Building ensemble models
    • Summary
  • Chapter 7: Model Understanding and Explainability
    • Reviewing and understanding model details
    • Assessing model performance and metrics
    • Generating model explanations
    • Understanding model learning curves and trade-offs
    • Summary
  • Chapter 8: Model Scoring and Deployment
    • Scoring and prediction methods
    • Generating prediction explanations
    • Analyzing predictions and postprocessing
    • Deploying DataRobot models
    • Monitoring deployed models
    • Summary
  • Section 3: Advanced Topics
  • Chapter 9: Forecasting and Time Series Modeling
    • Technical requirements
      • Appliances energy prediction dataset
    • Conceptual introduction to time series forecasting modeling
    • Defining and setting up time series projects
    • Building time series forecasting models and understanding their model outcomes
    • Making predictions with time series models
    • Advanced topics in time series modeling
    • Summary
  • Chapter 10: Recommender Systems
    • Technical requirements
      • Book-Crossing dataset
    • A conceptual introduction to recommender systems
    • Approaches to building recommender systems
      • Collaborative filtering recommender systems
      • Content-based recommender systems
      • Hybrid recommender systems
    • Defining and setting up recommender systems in DataRobot
    • Building recommender systems in DataRobot
    • Making recommender system predictions with DataRobot
    • Summary
  • Chapter 11: Working with Geospatial Data, NLP, and Image Processing
    • Technical requirements
      • House Dataset
    • A conceptual introduction to geospatial, text, and image data
      • Geospatial AI
      • Natural language processing
      • Image processing
    • Defining and setting up multimodal data in DataRobot
    • Building models using multimodal datasets in DataRobot
    • Making predictions using a multimodal dataset on DataRobot
    • Summary
  • Chapter 12: DataRobot Python API
    • Technical requirements
      • Automobile Dataset
    • Accessing the DataRobot API
    • Using the DataRobot Python client
      • Programming in Python using the Jupyter IDE.
    • Building models programmatically
    • Making predictions programmatically
    • Summary
  • Chapter 13: Model Governance and MLOps
    • Technical requirements
      • Book-Crossing dataset
    • Governing models
    • Addressing model bias and fairness
    • Implementing MLOps
    • Notifications and changing models in production
    • Summary
  • Chapter 14: Conclusion
    • Finding out additional information about DataRobot
    • Future of automated machine learning
    • Future of DataRobot
    • Why subscribe?
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