Model Serving Techniques - Learn Data Science with Travis

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🍮 Edge#147: MLOPs – Model Serving

TheSequence

In this issue: we explain what model serving is; we explore the TensorFlow serving paper; we cover TorchServe, a super simple serving framework for PyTorch. 💡 ML Concept of the Day: Model Serving Con...

🌀Edge#12: The challenges of Model Serving~

TheSequence

In this issue: we explain the concept of model serving; we review a paper in which Google Research outlined the architecture of a serving pipeline for TensorFlow models; we discuss MLflow, one of the ...

Several Ways for Machine Learning Model Serving (Model as a Service)

Towards AI

No matter how well you build a model, no one knows it if you cannot ship model. However, lots of data scientists want to focus on model building and skipping the rest of the stuff such as data…

Machine-learned model serving at scale

Towards Data Science

Imagine you have a machine-learned model that you would like to use in some application, for instance, a transformer model to generate vector representations from text. You measure the time it takes…

101 For Serving ML Models

Pratik’s Pakodas 🍿

Learn to write robust APIs Me at Spiti Valley in Himachal Pradesh → ML in production series Productionizing NLP Models 10 Useful ML Practices For Python Developers Serving ML Models My love for unders...

Gliding into Model-Based

Towards Data Science

Reinforcement Learning (RL) can be a daunting space to those new to the field due to terminology and complex mathematics formula. However, the principles underlying it are more intuitive than first…

Model serving architectures on Databricks

Marvelous MLOps Substack

Many different components are required to bring machine learning models to production. I believe that machine learning teams should aim to simplify the architecture and minimize the amount of tools th...

Models as Serverless Functions

Towards Data Science

I recently published Chapter 3 of my book-in-progress on leanpub. The goal with this chapter is to empower data scientists to leverage managed services to deploy models to production and own more of…

Models as Web Endpoints

Towards Data Science

In the second chapter of Data Science in Production, I discuss how to set up predictive models as web endpoints. This is a useful skill, because it enables data scientists to shift from batch model…

9. Model persistence

Scikit-learn User Guide

After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. The following sections give you some hints on how to persist a scik......

Model Deployment — Conclusion

Becoming Human: Artificial Intelligence Magazine

This is the concluding article of the Model Deployment Series. In this series we saw various techniques which can be used to deployed any ML model. We also touched the upper layer of the CICD via Git…...

Models and databases

Django documentation

A model is the single, definitive source of information about your data. It contains the essential fields and behaviors of the data you’re storing. Generally, each model maps to a single database tabl...