具體描述
Machine learning applications autonomously reason about data at massive scale. It’s important that they remain responsive in the face of failure and changes in load. And the best way to to keep applications responsive, resilient, and elastic is to incorporate reactive design. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. They also have unique challenges when you need to change the semantics or architecture of the system. To make machine learning systems reactive, you need to understand both reactive design patterns and modern data architecture patterns.
著者簡介
Jeff Smith builds large-scale machine learning systems using Scala and Spark. For the past decade, he has been working on data science applications at various startups in New York, San Francisco, and Hong Kong. He blogs and speaks about various aspect of building real world machine learning systems.
圖書目錄
1. Learning Reactive Machine Learning
1.1. An Example Machine Learning System
1.1.1. Building a Prototype System
1.1.2. Building a Better System
1.2. Reactive Machine Learning
1.2.1. Machine Learning
1.2.2. Reactive Systems
1.2.3. Traits of Reactive Machine Learning Systems
1.3. Summary
2. Using Reactive Tools
2.1. Scala, a Reactive Language
2.1.1. Reacting to Uncertainty in Scala
2.1.2. The Uncertainty of Time
2.2. Akka, a Reactive Toolkit
2.2.1. The Actor Model
2.2.2. Ensuring Resilience with Akka
2.3. Spark, a Reactive Big Data Framework
2.4. Summary
Part 2: Building a Reactive Machine Learning System
3. Collecting Data
3.1. Sensing Uncertain Data
3.2. Collecting Data at Scale
3.2.1. Maintaining State in a Distributed System
3.2.2. Understanding Data Collection
3.3. Persisting Data
3.3.1. Elastic and Resilient Databases
3.3.2. Fact Databases
3.3.3. Querying Persisted Facts
3.3.4. Understanding Distributed Fact Databases
3.4. Applications
3.5. Reactivities
3.6. Summary
4. Generating Features
5. Learning Models
6. Publishing Models
7. Predicting
Part 3: Operating a Reactive Machine Learning System
8. Delivering
9. Monitoring
10. Scaling
11. Evolving
· · · · · · (收起)
讀後感
評分
評分
評分
評分
用戶評價
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评分寫 ps 參考用 跟博客差不多
评分寫 ps 參考用 跟博客差不多
评分寫 ps 參考用 跟博客差不多
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