Serving layer for large machine learning models on Apache Flink
-
Updated
Aug 8, 2018 - Java
Serving layer for large machine learning models on Apache Flink
Demo fo mlserve project
developing a machine learning model with R and creating an API for it to enable it to connect with other applications and languages such as python and command line.
Reporting Analysis Results into a Word Document with R
Starter app for fastai v3 model deployment on Render
All the material from the Udemy course "Beyond Jupyter Notebooks"
REST API for training and prediction of seq2seq model
Worked on Machine learning algorithm using Decision Tree model and deployed model using Flask.
Hands on workshop material evaluating performance, fairness and robustness of models
Make use of PyTorch's custom modules to define a network architecture and train a model. Investigate how to improve a model's performance and deploy your model for wider use.
Jenkins
Run your own production inference code with Sagemaker
Covid-19 detection using Computer Vision from chest X-ray images, deployed on Flask server.
In this project, I have created simple model which predict the price of the house on the basis of it's area.
Add a description, image, and links to the model-deployment topic page so that developers can more easily learn about it.
To associate your repository with the model-deployment topic, visit your repo's landing page and select "manage topics."