FullStack AI Series - Intro to System Design for Data Scientists and ML Engineers
System design is the process of laying out a system's structure, components, modules, interfaces, and data to meet specified requirements. For machine learning engineers and data scientists, comprehending a system's life cycle provides a blueprint for building, deploying, and maintaining ML/AI solutions in production. This post will introduce and discuss some of the more critical stages of the system design process (including requirements analysis, architecture, development, deployment, and scaling). It will also introduce some technologies and tools that can be used to design, develop, and deploy systems, such as Docker, Docker Compose, Docker Swarm, and Kubernetes.
, 35 min read
Python for Data Science Series - Exploring the syntax
In the last post, we discussed the importance of programming in the data science context and why Python is considered one of the top languages used by data scientists. In this week's post, we will explore the syntax of Python and create a simple program that uses Google Cloud Vision API to detect faces in an image.
, 20 min read
Python for Data Science Series - Getting started
Thinking about jumping into a data science role, but you don't know why you should learn how to program and which programming language to choose? In this post, I will show you how to use python and discuss why this programming language is considered one of the top used in data science.
, 7 min read
Python for Data Science Series - Python environments with pyenv and poetry
If you have been using Python for a while or just started, you may have already noticed that handling different python-installations and dependencies(packages) can be a nightmare! So, having tools that enable us to isolate and manage our project's dependencies is highly convenient. In this post, I will show you how to use pyenv and poetry to create your code environments.
, 19 min read