Production Machine Learning Systems

This course is part of multiple programs. Learn more

Instructor: Google Cloud Training

What you'll learn

  •   Compare static versus dynamic training and inference
  •   Manage model dependencies
  •   Set up distributed training for fault tolerance, replication, and more
  •   Export models for portability
  • Skills you'll gain

  •   Performance Tuning
  •   Machine Learning
  •   Hybrid Cloud Computing
  •   Systems Design
  •   Tensorflow
  •   Data Pipelines
  •   MLOps (Machine Learning Operations)
  •   Google Cloud Platform
  •   Applied Machine Learning
  •   Scalability
  •   Distributed Computing
  •   Systems Architecture
  • There are 6 modules in this course

    In this course, we dive into the components and best practices of building high-performing ML systems in production environments. We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs. This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions.

    Architecting Production ML Systems

    Designing Adaptable ML Systems

    Designing High-Performance ML Systems

    Building Hybrid ML Systems

    Summary

    Explore more from Machine Learning

    ©2025  ementorhub.com. All rights reserved