MLOps: CI/CD for Machine Learning培训

课程编码

mlops

课程时长

35 小时 通常来说是5天,包括中间休息。

要求

  • An understanding of the software development cycle
  • Experience building or working with Machine Learning models
  • Familiarity with Python programming

Audience

  • ML engineers
  • DevOps engineers
  • Data engineers
  • Infrastructure engineers
  • Software developers

课程概览

MLOps is a set of tools and methodologies for combining Machine Learning and DevOps practices. The goal of MLOps is to automate and optimize the deployment and maintenance of ML systems in production.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

By the end of this training, participants will be able to:

  • Install and configure various MLOps frameworks and tools.
  • Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
  • Prepare, validate and version data for use by ML models.
  • Understand the components of an ML Pipeline and the tools needed to build one.
  • Experiment with different machine learning frameworks and servers for deploying to production.
  • Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

课程大纲

Introduction

  • Machine Learning models vs traditional software

Overview of the DevOps Workflow

Overview of the Machine Learning Workflow

ML as Code Plus Data

Components of an ML System

Case Study: A Sales Forecasting Application

Accessing Data

Validating Data

Data Transformation

From Data Pipeline to ML Pipeline

Building the Data Model

Training the Model

Validating the Model

Reproducing Model Training

Deploying a Model

Serving a Trained Model to Production

Testing an ML System

Continuous Delivery Orchestration

Monitoring the Model

Data Versioning

Adapting, Scaling and Maintaining an MLOps Platform

Troubleshooting

Summary and Conclusion

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