After defining some of the main concepts in the API world in the previous article, I will talk about the different ways of deploying an API Gateway for the Machine Learning platform.
In this article, I will use the infrastructure and software layers designed in one of my previous articles. You may want to go through it to have a clearer view of the platform’s architecture before proceeding.
As a reminder, the scope of this series of articles is the model serving layer of the ML platform’s framework layer. In other words, its “API Gateway”.
After defining the framework layer of a custom machine learning (ML) platform on AWS, it’s time to talk in detail about some specific scopes of this layer.
The scope of this series of articles is the model serving layer of the framework. In other words, its “API Gateway”.
This is the fourth chapter of my journey in building a Machine Learning Platform on AWS. This chapter is based on my work so far presented in the previous parts: the high-level overview of the ML Platform, the infrastructure & software layers, and the framework layer.
In this part, I am going to study the fourth layer of the Machine Learning Platform on AWS: Use cases layer.
When a new use case is selected by stakeholders, comes the ultimate goal of landing this use case on the machine learning platform. Achieving this goal is constrained by solving three riddles:
This is the third piece of my journey in building a machine learning (ML) platform on AWS and a continuity of the high-level overview presented in the first article as well as the Infrastructure and Software layers demystified in the second one.
In this part, I am going to study the third layer of the ML platform: the framework layer.
By definition, a framework is “an abstraction […] providing generic functionality.” ¹ In other words, it is a high-level layer that hides the tricky details of the platform’s software stack and exposes user-friendly functionalities.
Amazon understood this very well. That…
This is the second slice of my journey in building a machine learning (ML) platform on AWS and a continuity of the high-level overview presented in the first article.
In this part, I am going to study in detail the first two layers of the ML platform: infrastructure and software layers.
A good design for the infrastructure and software layers should respond to many challenges:
Answering all these challenges is…
So, I decided to put it to the test: I tried to build a Machine Learning Platform on AWS.
The result? This certification is definitely worth it: Properly preparing and passing it gave me the necessary tools to look at the big picture, see all the little moving parts. But it is not enough… In order to achieve this, I got inspired by what big companies with great expertise in this domain did, like Uber with their Michelangelo, Netflix, Comcast, and many others. …
Passionate Data Architect with progressive experience in building big data platform at @CreditAgricole and machine learning platform on AWS