This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
Production Machine Learning Systems
Über diesen Kurs
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Was Sie lernen werden
Compare static versus dynamic training and inference
Manage model dependencies
Set up distributed training for fault tolerance, replication, and more
Export models for portability
Könnte Ihr Unternehmen von Mitarbeiterweiterbildungen für gefragte Kompetenzen profitieren?Probieren Sie Coursera for Business aus
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Lehrplan - Was Sie in diesem Kurs lernen werden
Introduction to Advanced Machine Learning on Google Cloud
This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
Architecting Production ML Systems
This module explores what else a production ML system needs to do and how to meet those needs. You review how to make important, high-level, design decisions around training and model serving need to make in order to get the right performance profile for your model.
Designing Adaptable ML Systems
In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
Designing High-Performance ML Systems
In this module, you identify performance considerations for machine learning models.
Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
Building Hybrid ML Systems
Understand the tools and systems available and when to leverage hybrid machine learning models.
This module reviews what you learned in this course.
- 5 stars70,24 %
- 4 stars21,69 %
- 3 stars5,80 %
- 2 stars1,50 %
- 1 star0,75 %
Top-Bewertungen von PRODUCTION MACHINE LEARNING SYSTEMS
The course provides insight into applying Machine Learning system in actual environment, with real-life examples and lab assistance.
Excellent overview of designing real-world ML systems. Some of the labs are daunting, but the emphasis is showing you what can be achieved, rather than achieving mastery within the course.
Really focuses on topics for building production ML Systems
I did not realize the many aspects to consider implementing a Production ML system. This course presents all of them and provides guidance for evaluating alternative
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Dieser Kurs ist einer der wenigen auf Coursera angebotenen Kurse, die derzeit ausschließlich für Kursteilnehmer zugänglich sind, die bezahlt oder finanzielle Unterstützung erhalten haben, wenn diese verfügbar ist.
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Last Updated: September 1, 2015
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