Bentoml

A Conversation with BentoML’s Founder & CEO Chaoyu Yang — In this live stream, we welcomed Chaoyu Yang, Founder & CEO at BentoML on the R2AI Podcast to discuss what it takes to put machine learning models in production and BentoML’s role in simplifying deployment. …

All the steps needed to set up Yatai 1.0 on GKE. — At DKatalis, we are slowly but surely migrating our existing machine-learning models from FastAPI and KNative to BentoML and Yatai. I’ve previously written about my impressions of BentoML and setting up automatic model deployment with BentoML, so you might want to take a look at those too. The purpose of…

Containerize and Deploy Your ML Model in Python Motivation You have just built a machine learning model to predict which group a customer belongs to. The model seems to do a good job in segmenting your customers. …

How to automate model deployment and even model retraining in your own setup. — One of the things that I’ve been dissatisfied with so far with our current workflow is that model deployment is not as automated as it could be. This is partly because in model deployment there are several things to consider: How the model was built, which affects how the model…

As a Machine Learning Engineer, I’m always on the lookout for tools and processes that would improve the quality of Data Scientists. One area that I’ve found especially lacking is that most Data Scientists are not familiar with the details surrounding deploying their models. Model development shouldn’t just be simply…

ML/DL model is trained and ready, but is it really ready to be moved to production? Data Science Teams and the Data Scientists in an organisation come up with ML/DL models needed for their use cases, building either custom or fine-tuned pre-trained models, with a reasonably quick turnaround. However it…

Introduction In the modern tech scene, microservice architecture has become the norm for engineering teams to surface their work (services), which is later consumed by others. Same goes for data products such as machine learning models and recommendation systems. In this article, we will share the challenges that we encountered and…

In this blog post, we will be packaging our previously created Titanic Survival Model and building a REST-based prediction service on the model. The process is fairly generic and can be adapted to any Sci-kit model Starting from where we left of, we have so far been able to fit…

BentoML is currently one of the hottest frameworks for serving, managing and deploying machine learning models. If you are reading this blog, I guess you have heard of it and have some interest in checking it out. So was I, and that’s why I did this small experiment. One of…