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Articles by Marco Susilo
Factors When Designing Modern Applications Part 3
By Marco Susilo When designing modern applications or software-as-a-service (SaaS) apps a multi step methodology can be utilised to ensure ideal/best practices are maintained during development. Photo by luca fantini on Unsplash This article is Part 3 of a 4 Part series outlining the methods modern applications can use during development. The methodology can be used when developing applications regardless of the choice of...
Modelling Many to Many Relationships in Amazon DynamoDB
Amazon DynamoDB is a managed and highly scalable NoSQL database service in AWS. It is very effective when working with a online transaction processing (OLTP) application. Relational Database Management Systems (RDBMS) already exists and can also be used for such applications, however, this technology was created at a time where data storage costs was relatively high when compared to data processing costs.
MLOps Level 0 - Manual Pipelines
MLOps level 0 is the basic level of maturity of a machine learning process. For some organisations this may be sufficient, especially if models are infrequently required to be re-trained. This level of MLOps is characterised by its highly manual steps within the workflow. To address the challenges prevalent in MLOps level 0 a continuous integration, continuous deployment and continuous training component can be utilised.
Steps of a Machine Learning Process
A machine learning process is made up of several steps which are cyclical in nature. The more of these steps an organisation can automate through MLOps the more mature the machine learning process is. Photo by Joshua Sortino on Unsplash MLOps is about the application of DevOps philosophy into a machine learning system (to read more about these two practices please checkout my article: https://towardsdatascience.com/mlops-vs-devops-5c9a4d5a60ba).
MLOps vs DevOps
Photo by HalGatewood.com on Unsplash As the field of machine learning has matured in recent years, the need for integrating automatic continuous integration (CI), continuous delivery (CD) and continuous training (CT) to machine learning systems has increased. The application of DevOps philosophy to a machine learning system has been termed MLOps. The aim of MLOps is to fuse together the machine learning system development (ML) and machine learning system operation (Ops) together. What is DevOps?
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