book

Low-Code AI

by Gwendolyn Stripling, Michael Abel

September 2023

Intermediate to advanced

328 pages

8h 47m

English

O'Reilly Media, Inc.

Audiobook available

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Includes

Quizzes

Preface
Who Should Read This Book?What Is and Isn’t in This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. How Data Drives Decision Making in Machine Learning
What Is the Goal or Use Case?An Enterprise ML WorkflowDefining the Business Objective or Problem StatementData CollectionData PreprocessingData AnalysisData Transformation and Feature SelectionResearching the Model Selection or Using AutoML (a No-Code Solution)Model Training, Evaluation, and TuningModel TestingModel Deployment (Serving)Maintaining ModelsSummary
2. Data Is the First Step
Overview of Use Cases and Datasets Used in the Book1. Retail: Product Pricing2. Healthcare: Heart Disease Campaign3. Energy: Utility Campaign4. Insurance: Advertising Media Channel Sales Prediction5. Financial: Fraud Detection6. Energy: Power Production Prediction7. Telecommunications: Customer Churn Prediction8. Automotive: Improve Custom Model PerformanceData and File TypesQuantitative and Qualitative DataStructured, Unstructured, and Semistructured DataData File TypesHow Data Is ProcessedAn Overview of GitHub and Google’s ColabUse GitHub to Create a Data Repository for Your ProjectsUsing Google’s Colaboratory for Low-Code AI ProjectsSummary
3. Machine Learning Libraries and Frameworks
No-Code AutoMLHow AutoML WorksMachine Learning as a ServiceLow-Code ML FrameworksSQL ML FrameworksOpen Source ML LibrariesSummary
4. Use AutoML to Predict Advertising Media Channel Sales
The Business Use Case: Media Channel Sales PredictionProject WorkflowProject DatasetExploring the Dataset Using Pandas, Matplotlib, and SeabornLoad Data into a Pandas DataFrame in a Google Colab NotebookExplore the Advertising DatasetUse AutoML to Train a Linear Regression ModelNo-Code Using Vertex AICreate a Managed Dataset in Vertex AISelect the Model ObjectiveBuild the Training ModelEvaluate Model PerformanceModel Feature Importance (Attribution)Get Predictions from Your ModelSummary
5. Using AutoML to Detect Fraudulent Transactions
The Business Use Case: Fraud Detection for Financial TransactionsProject WorkflowProject DatasetExploring the Dataset Using Pandas, Matplotlib, and SeabornLoading Data into a Pandas DataFrame in a Google Colab NotebookExploring the DatasetExporting the DatasetClassification Models and MetricsUsing AutoML to Train a Classification ModelCreating a Managed Dataset and Selecting the Model ObjectiveExploring Dataset StatisticsTraining the ModelEvaluating Model PerformanceModel Feature ImportancesGetting Predictions from Your ModelSummary
6. Using BigQuery ML to Train a Linear Regression Model
The Business Use Case: Power Plant ProductionCleaning the Dataset Using SQL in BigQueryLoading a Dataset into BigQueryExploring Data in BigQuery Using SQLLinear Regression ModelsFeature Selection and CorrelationGoogle ColaboratoryPlotting Feature Relationships to the LabelThe CREATE MODEL Statement in BigQuery MLIntroducing Explainable AIExplainable AI in BigQuery MLExercisesNeural Networks in BigQuery MLBrief Overview of Neural NetworksActivation Functions and NonlinearityTraining a Deep Neural Network in BigQuery MLExercisesDeep Dive: Using Cloud Shell to View Your Cloud Storage FileSummary
7. Training Custom ML Models in Python
The Business Use Case: Customer Churn PredictionChoosing Among No-Code, Low-Code, or Custom Code ML SolutionsExploring the Dataset Using Pandas, Matplotlib, and SeabornLoading Data into a Pandas DataFrame in a Google Colab NotebookUnderstanding and Cleaning the Customer Churn DatasetTransforming Features Using Pandas and Scikit-LearnBuilding a Logistic Regression Model Using Scikit-LearnLogistic RegressionTraining and Evaluating a Model in Scikit-LearnClassification Evaluation MetricsServing Predictions with a Trained Model in Scikit-LearnPipelines in Scikit-Learn: An IntroductionBuilding a Neural Network Using KerasIntroduction to KerasTraining a Neural Network Classifier Using KerasBuilding Custom ML Models on Vertex AISummary
8. Improving Custom Model Performance
The Business Use Case: Used Car Auction PricesModel Improvement in Scikit-LearnLoading the Notebook with the Preexisting ModelLoading the Datasets and the Training-Validation-Test Data SplitExploring the Scikit-Learn Linear Regression ModelFeature Engineering and Improving the Preprocessing PipelineHyperparameter TuningModel Improvement in KerasIntroduction to Preprocessing Layers in KerasCreating the Dataset and Preprocessing Layers for Your ModelBuilding a Neural Network ModelHyperparameter Tuning in KerasHyperparameter Tuning in BigQuery MLLoading and Transforming Car Auction DataTraining a Linear Regression Model and Using the TRANSFORM ClauseConfigure a Hyperparameter Tuning Job in BigQuery MLOptions for Hyperparameter Tuning Large ModelsVertex AI Training and TuningAutomatic Model Tuning with Amazon SageMakerAzure Machine LearningSummary
9. Next Steps in Your AI Journey
Going Deeper into Data ScienceWorking with Unstructured DataGenerative AIExplainable AIML OperationsContinuous Training and EvaluationSummary

Index
About the Authors

Content preview from Low-Code AI

Chapter 6. Using BigQuery ML to Train a Linear Regression Model

In this chapter you learn how to build a linear regression model and a neural network model from scratch to forecast power plant production. You perform this task using SQL for data analysis, Jupyter Notebook for data exploration, and BigQuery Machine Learning (BigQuery ML) for training the ML model. Along the way, you learn new techniques for understanding your data in preparation for ML and how to apply this knowledge in improving your model performance.

The Business Use Case: Power Plant Production

Your goal in this project will be to predict the net hourly electrical energy output for a combined cycle power plant (CCPP) given the weather conditions near the plant at the time.

A CCPP is composed of gas turbines, steam turbines, and heat recovery steam generators. The electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the vacuum is collected from the steam turbine, the other three ambient variables (temperature, ambient pressure, and relative humidity) affect the gas turbine performance.

The dataset in this section contains data points collected from a CCPP over a six-year period (2006–2011) when the power plant was set to work with a full load. The data is aggregated per hour, though the exact hour for the recorded weather conditions and energy production is not supplied in the dataset. From a practical viewpoint, this means ...

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Low-Code AI

by Gwendolyn Stripling, Michael Abel

Chapter 6. Using BigQuery ML to Train a Linear Regression Model

The Business Use Case: Power Plant Production

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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