Feature Stores: Centralized Feature Management
Enterprise feature stores. Learn the architecture, implementation strategies, and business value of centralizing feature management for ML.
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AI Engineering Volume 2
Automated Feature Engineering and Discovery
Learn to implement Deep Feature Synthesis with Featuretools, understand genetic programming, and deploy scalable ML pipelines.
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Feature Selection: Statistical and Model-based Methods
Learn statistical and model-based methods like RFE and Lasso, and implement them in Python with scikit-learn to improve model performance.
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Feature Engineering: Creating New Features from Raw Data
Learn to create polynomial features, interaction terms, and temporal features using Python, scikit-learn, and pandas to improve model performance.
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Automated Data Quality Monitoring
Automated data quality monitoring systems for production ML pipelines. Learn to detect data drift, validate schemas, and build robust MLOps workflows.
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Data Validation and Quality Assurance for Machine Learning
Automated data validation and quality assurance pipelines. Learn to use Pandera and Great Expectations to prevent bad data from impacting model performance.
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Outlier Detection and Treatment Methods
Outlier detection and treatment for AI. Learn statistical methods, Isolation Forest, and DBSCAN with Python examples to build robust machine learning models.
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Data Cleaning Techniques: Handling Missing Data
Handling missing data in AI engineering. Learn theoretical foundations (MCAR, MAR, MNAR) and practical data cleaning techniques.
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Real-time Data Streaming Architecture
Design and implement real-time data streaming architectures for machine learning using Apache Kafka and Apache Storm, with practical Python examples.
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Data Privacy and Compliance (GDPR, CCPA)
Data privacy in AI. Learn to implement GDPR and CCPA compliance, use anonymization, differential privacy, and federated learning.
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