Feature Engineering and Feature Stores for AI and ML

अवलोकन

This course focuses on preparing AI-ready data through feature engineering, feature management, and pipeline automation. You will learn how data engineers create high-quality features, organise reusable feature assets, and automate workflows that support scalable machine learning systems.

You will begin by exploring the principles of feature engineering and learn how to transform raw datasets into meaningful features for machine learning. Through practical exercises, you will create numerical, categorical, and derived features while applying techniques such as scaling, encoding, and skewness handling to improve model performance.

Next, you will discover how Feature Stores enable consistent and reusable feature management across AI projects. You will design feature table schemas, manage structured and text-based features, generate embeddings, and store AI-ready features in Databricks for efficient reuse across multiple machine learning workflows.

You will also learn how machine learning workflows consume engineered data by preparing training, validation, and test datasets, while using MLflow to track datasets, experiments, and model development for reproducibility and collaboration. Finally, you will automate end-to-end AI/ML data pipelines using Databricks Jobs.

You will structure notebook-based workflows into production pipelines, schedule and monitor multi-task jobs, and orchestrate reliable data engineering processes that support enterprise-scale AI applications. By the end of this course, you will be able to:

- Engineer high-quality features for machine learning applications. - Build and manage reusable Feature Stores in Databricks. - Prepare and track ML datasets using MLflow. - Automate AI/ML workflows using Databricks Jobs. - Develop scalable data pipelines for production AI systems.

Designed for data engineers, machine learning engineers, data scientists, and AI professionals, this course equips you with the practical skills to build feature-driven, automated, and production-ready AI/ML data pipelines using modern data engineering practices.

पाठ्यक्रम

• Feature Engineering for ML Readiness
Master the process of transforming curated data into machine learning-ready features for predictive modelling. This module introduces the fundamentals of feature engineering, including creating numerical, categorical, recency, and tenure features, while preparing data for downstream ML workflows. Through hands-on exercises, you'll apply techniques such as scaling, encoding, and skewness handling to build high-quality feature sets that improve model performance and reliability.
• Feature Stores and AI-Ready Features
Dive into feature tables and modern Feature Store concepts for managing machine learning features at scale. This module explores feature table schema design for structured and text data, feature table creation in Databricks, and the use of text features and LLM embeddings. Through hands-on exercises, you will extract text features, store embeddings in Delta tables, and examine online and offline Feature Stores for scalable AI/ML applications.
• Supplying Data to ML Workflows
Explore how data engineers prepare and deliver reliable datasets for machine learning workflows. This module focuses on creating training, validation, and test datasets, tracking feature datasets with MLflow, and supporting collaboration between data engineering and ML teams. Through hands-on exercises, you will build reproducible data pipelines that support efficient model development and deployment.
• Pipeline Automation and Orchestration
Uncover how to automate, orchestrate, and manage end-to-end AI/ML data pipelines using Databricks. This module covers structuring data projects, building multi-task workflows, scheduling and monitoring pipeline execution, and exploring orchestration tools such as Databricks Workflows, Airflow, and dbt. Through hands-on exercises, you will develop reliable, automated pipelines for scalable and production-ready AI/ML solutions.

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द्वारा पढ़ाया गया

Edureka

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विषय

Artificial Intelligence