To inspire others, grow your personal brand, and get paid for it by writing articles and tutorials about machine learning in production as the MLOps blog contributor!
Our articles reach hundreds of viewers each month who want to learn more about topics in AI. We are constantly getting feedback and inquiries for new materials and resources, so you will definitely have a lot to choose from.
In addition, all of our writers get support and feedback. We're looking for materials that are educational, insightful, and help people learn more about MLOps and related topics.
More and more companies decide to implement AI solutions in production. However, due to the fact that Machine Learning projects differentiate significantly from traditional ones lots of those projects never see the light and fail before the final release. Therefore, we are looking for a great writer/creator/engineer. Somebody who will help us bring all those ML projects to production. Somebody like you!
We do believe that MLOps is the Next Big Thing in the whole AI ecosystem. Therefore in Syndicai, we want to inspire others and help them implement best MLOps practices. As an MLOps blog contributor, you will be able to bring that mission to life.
What exactly should I write?
If you are passionate about machine learning in production we are more than happy to work with you! Our main goal is to inspire organizations to build, create and deploy outstanding models able to solve challenging problems. Therefore we are looking mainly for topics in the field of MLOps, Machine Learning in Production, Deep Learning, Cloud infrastructure, AI model deployments, etc.
We are mostly interested in the following topics:
How to ... (How to deploy a Pytorch model in minutes)
Best practices ... (Best NLP model deployment practices)
Top X ... (Top 10 platforms for AI monitoring at scale)
Recent ... (What exactly is GPT-3, and why is so hot? )
A quick guide to ... (A quick guide to train & deploy a YoloV5 in production)
How to ... (How to optimize model inference at scale?)
Deploy a ... (Deploy a GPT-3 at scale in minutes)
Things to care about... (Things to care about when setting up the k8s cluster)