Ementorhub

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

Applied Machine Learning

MLOps (Machine Learning Operations)

Distributed Computing

Hybrid Cloud Computing

Systems Architecture

Scalability

Performance Tuning

Machine Learning

Data Pipelines

Tensorflow

Google Cloud Platform

Systems Design

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

Machine Learning in Production

Operationalizing ML Models: MLOps for Scalable AI

Foundations of AI and Machine Learning

Advanced Machine Learning Algorithms