Community Picks·4yThe Guide to Modern Data Architecture
This is an updated version of a post we originally published in 2020. We argue that core data processing systems have remained relatively stable over the past year, while supporting tools and applications have proliferated rapidly. We explore the hypothesis that platforms are beginning to emerge in the data ecosystem, and that this helps explain the particular patterns we’re seeing.
Towards Data Science·4yHow I Set Up My MacBook Pro As A ML Engineer in 2022
How I Set Up My MacBook Pro As A ML Engineer in 2022 A guide to getting your MacBook ready for ML development. These are the things I do to setup my MacBook as a ML Engineer. This article is inspired by Vinicius — do check out his article here!
Geek Culture·4yWhy You need Math for Machine Learning
A lot of Deep Learning Gurus on Twitter make claims like this. Not learning at least some Math BEFORE you get into ML will actively hurt you. Almost no Deep Learning engineer uses Fourier Series, Number Transformations, Calculus, or anything fancy regularly. You can train, test, evaluate, deploy, and even cross-validate models without any Mathematical/Theoretical Foundations.
Community Picks·4yGo faster with Go: Golang for ML Serving
The current Python service with single node can do 192 RPS, about 400 pairs each. Only about 20% average CPU utilization. The limiting factor now was the language, the serving framework and the network call to feature store. I chose Fiber framework for REST API, it seemed most welcoming, good documentation, expressjs like API. Took less than an hour.
AWS Tip·4yAmazon Lex
Amazon Lex is an easy to use chatbot building tool on AWS which powers the Amazon Alexa voice assistance. Any developer can easily build and deploy chatbot in your website or mobile application or Facebook messenger or Instagram etc. You can use Lex for higher fidelity telephone speech interactions through a contact center application or helpdesk.