Course Outline

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

Requirements

  • 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
 35 Hours

Number of participants



Price per participant

Testimonials (3)

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