null Book

Chapter 1: Introduction to MLOps Strategies and Best Practices

Chapter 2: Advantages of Using ML Runtimes

Chapter 3: Role of AutoML in Model and Feature Selection

Chapter 4: Enhancing Model Development with AutoML

Chapter 5: Unity Catalog: Account-Level vs. Workspace-Level Feature Store Tables

Chapter 6: Creating Feature Store Tables in Unity Catalog

Chapter 7: Writing Data to Feature Store Tables

Chapter 8: Training Models Using Feature Store Tables

Chapter 9: Scoring Models with Features from Feature Store Tables

Chapter 10: Online vs. Offline Feature Tables: Key Differences

Chapter 11: Using the MLflow Client API to Identify the Best Run

Chapter 12: Manually Logging Metrics, Artifacts, and Models in MLflow

Chapter 13: Exploring the MLflow UI for Model Insights

Chapter 14: Registering Models in Unity Catalog with the MLflow Client API

Chapter 15: Benefits of Unity Catalog Registry over Workspace Registry

Chapter 16: Promoting Code vs. Promoting Models: Scenarios and Preferences

Chapter 17: Setting and Removing Tags for Models

Chapter 18: Promoting Challenger Models to Champion Models Using Aliases

Chapter 19: Computing Summary Statistics with Spark DataFrames

Chapter 20: Removing Outliers Using Standard Deviation or IQR

Chapter 21: Creating Visualizations for Categorical and Continuous Features

Chapter 22: Comparing Categorical or Continuous Features

Chapter 23: Imputing Missing Values: Mean, Median, and Mode Approaches

Chapter 24: Using One-Hot Encoding for Categorical Features

Chapter 25: Scenarios for One-Hot Encoding: Applicability and Limitations

Chapter 26: Log Scale Transformations: When and Why to Use

Chapter 27: Choosing the Right Algorithm with ML Foundations

Chapter 28: Addressing Data Imbalance in Training Data

Chapter 29: Estimators vs. Transformers: Key Comparisons

Chapter 30: Building and Managing Training Pipelines

Chapter 31: Tuning Hyperparameters with Hyperopt's fmin Operation

Chapter 32: Methods for Hyperparameter Tuning: Random, Grid, and Bayesian Searches

Chapter 33: Parallelizing Hyperparameter Tuning for Single-Node Models

Chapter 34: Cross-Validation vs. Train-Validation Split: Benefits and Tradeoffs

Chapter 35: Performing Cross-Validation During Model Fitting

Chapter 36: Calculating Number of Models in Grid-Search and Cross-Validation

Chapter 37: Using Classification Metrics: F1, Log Loss, ROC/AUC

Chapter 38: Regression Metrics: RMSE, MAE, R-squared, and Their Use Cases

Chapter 39: Choosing Metrics for Specific Scenario Objectives

Chapter 40: Exponentiating Log-Transformed Variables Before Evaluation

Chapter 41: Model Complexity and Bias-Variance Tradeoff Impacts

Chapter 42: Batch, Real-Time, and Streaming Model Serving: Comparisons and Benefits

Chapter 43: Deploying Custom Models to Endpoints

Chapter 44: Performing Batch Inference with pandas

Chapter 45: Streaming Inference Using Delta Live Tables

Chapter 46: Real-Time Model Deployment and Querying

Chapter 47: Splitting Data Across Endpoints for Real-Time Inference

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