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Mastering ML Data Pipelines: Feature Stores, Orchestration, and Monitoring Explained
Mastering ML Data Feature Stores, Orchestration, and Monitoring Explained
Are your ML models starved for reliable, real-time data? What if every feature, alert, and workflow ran like clockwork—no surprises, just results?
Summary
Mastering ML Data Pipelines shows you how to architect and operate production-grade pipelines that feed AI agents with up-to-the-second features. From batch processing and streaming ingestion to feature stores, orchestration frameworks, and end-to-end monitoring, this book delivers hands-on patterns and code you can use today.
What Sets This Book Apart?
Rather than broad theory, you get practical chapters packed with implementable
Chapter 1: Foundations of ML Data Engineering – Understand batch vs. streaming, performance metrics, and where pipelines fit in AI workflows.
Chapter 2: Data Ingestion Strategies – Connect to databases, message queues, and data lakes; handle schema evolution.
Chapter 5: Feature Stores in Practice – Build and scale Feast (and alternatives) for online and offline feature serving.
Chapter 6: Workflow Orchestration and Scheduling – Design resilient DAGs in Airflow, Prefect’s task-first flows, and Kubeflow pipelines.
Chapter 7: Streaming Pipelines and Real-Time Serving – Compute rolling features with Kafka Streams, Flink, and integrate with Feast.
Chapter 9: Monitoring, Observability, and Alerting – Instrument Prometheus/Grafana, structured logging, distributed tracing, and automated remediation.
Chapter 11: Testing and CI/CD for Data Pipelines – Mock data sources, validate sinks, adopt GitOps, and implement canary releases.
Chapter 12: Cost Optimization and Scalability – Autoscale clusters, partition and shard data, and decide between serverless and clustered deployments.
Chapter 14: Emerging Trends and the Future – Explore data mesh, self-driving pipelines with AI agents, and unified observability platforms.
Each chapter blends expert commentary with personal insights—so you’ll learn not only how to build pipelines, but how to avoid the pitfalls that sideline most projects.
Ready to transform your ML workflows into reliable, scalable, and observable systems? Grab your copy of Mastering ML Data Pipelines and start building the next generation of data-driven AI today.
Are your ML models starved for reliable, real-time data? What if every feature, alert, and workflow ran like clockwork—no surprises, just results?
Summary
Mastering ML Data Pipelines shows you how to architect and operate production-grade pipelines that feed AI agents with up-to-the-second features. From batch processing and streaming ingestion to feature stores, orchestration frameworks, and end-to-end monitoring, this book delivers hands-on patterns and code you can use today.
What Sets This Book Apart?
Rather than broad theory, you get practical chapters packed with implementable
Chapter 1: Foundations of ML Data Engineering – Understand batch vs. streaming, performance metrics, and where pipelines fit in AI workflows.
Chapter 2: Data Ingestion Strategies – Connect to databases, message queues, and data lakes; handle schema evolution.
Chapter 5: Feature Stores in Practice – Build and scale Feast (and alternatives) for online and offline feature serving.
Chapter 6: Workflow Orchestration and Scheduling – Design resilient DAGs in Airflow, Prefect’s task-first flows, and Kubeflow pipelines.
Chapter 7: Streaming Pipelines and Real-Time Serving – Compute rolling features with Kafka Streams, Flink, and integrate with Feast.
Chapter 9: Monitoring, Observability, and Alerting – Instrument Prometheus/Grafana, structured logging, distributed tracing, and automated remediation.
Chapter 11: Testing and CI/CD for Data Pipelines – Mock data sources, validate sinks, adopt GitOps, and implement canary releases.
Chapter 12: Cost Optimization and Scalability – Autoscale clusters, partition and shard data, and decide between serverless and clustered deployments.
Chapter 14: Emerging Trends and the Future – Explore data mesh, self-driving pipelines with AI agents, and unified observability platforms.
Each chapter blends expert commentary with personal insights—so you’ll learn not only how to build pipelines, but how to avoid the pitfalls that sideline most projects.
Ready to transform your ML workflows into reliable, scalable, and observable systems? Grab your copy of Mastering ML Data Pipelines and start building the next generation of data-driven AI today.
242 pages, Kindle Edition
Published May 26, 2025
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