TensorFlow Serving 교육 과정

Course Code

tensorflowserving

Duration

7 hours (usually 1 day including breaks)

Requirements

  • Experience with TensorFlow
  • Experience with the Linux command line

Audience

  • Developers
  • Data scientists

Overview

TensorFlow Serving은 기계 학습 (ML) 모델을 프로덕션에 제공하기위한 시스템입니다.

강사가 진행하는이 실시간 교육에서 참가자는 프로덕션 환경에서 ML 모델을 배포 및 관리하기 위해 TensorFlow Serving을 구성하고 사용하는 방법을 학습합니다.

이 훈련이 끝나면 참가자는 다음을 할 수 있습니다.

  • 다양한 TensorFlow 모델 교육, 수출 및 제공
  • 단일 아키텍처 및 API 세트를 사용하여 알고리즘 테스트 및 배포
  • 확장 TensorFlow 넘어 모델의 다른 종류의 봉사 봉사 TensorFlow 모델을

청중

  • 개발자
  • 데이터 과학자

과정 형식

  • 파트 강의, 파트 토론, 연습 및 무거운 실무 연습

Machine Translated

Course Outline

TensorFlow Serving Overview

  • What is TensorFlow Serving?
  • TensorFlow Serving architecture
  • Serving API and REST client API

Preparing the Development Environment

  • Installing and configuring Docker
  • Installing ModelServer with Docker

TensorFlow Server Quick Start

  • Training and exporting a TensorFlow model
  • Monitoring storage systems
  • Loading exported model
  • Building a TensorFlow ModelServer

Advanced Configuration

  • Writing a config file
  • Reloading Model Server configuration
  • Configuring models
  • Working with monitoring configuration

Testing the Application

  • Testing and running the server

Debugging the Application

  • Handling errors

TensorFlow Serving with Kubernetes

  • Running in Docker containers
  • Deploying serving clusters

Securing the Application

  • Hiding data

Troubleshooting

Summary and Conclusion

회원 평가

★★★★★
★★★★★

Related Categories

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