In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments.
Prerequisites: Basic SQL, familiarity with Python and TensorFlow
Welcome to the course
In this module we will preview the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud Platform.
Architecting Production ML Systems
In this module, we’ll talk about what else a production ML system needs to do and how you can meet those needs. We’ll then review some important, high-level, design decisions around training and model serving that you’ll need to make in order to get the right performance profile for your model.
Ingesting data for Cloud-based analytics and ML
In this module, we’ll talk about how to bring your data to the cloud. There are many ways to bring your data into cloud to power your machine learning models. We’ll first review why your data needs to be on the cloud to get the advantages of scale and using fully-managed services and what options you have to bring your data over.
Designing Adaptable ML systems
In this module, we’ll learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
Designing High-performance ML systems
In this module, you will learn how to identify performance considerations for machine learning models.
Machine learning models are not all identical. For some models, you will be focused on improving I/O performance, and on others, you will be focused on squeezing out more computational speed.
Hybrid ML systems
Understand the tools and systems available and when to leverage hybrid machine learning models.
Review the content covered in the modules on Production ML systems