End-to-End Image Captioning

2021-02-10T08:54:02+00:00

We had the unique opportunity to develop an image captioning system combining computer vision and NLP from a prototype model to a fully scalable data product with a team of five interdisciplinary students from the TUM Data Innovation Lab during a period of six months as part of an educational research experience.

tl;dr Data Science, Machine Learning Engineering, Software Engineering, and IT-Operations know-how is required to turn a prototypical machine-learning model into an end-

End-to-End Image Captioning2021-02-10T08:54:02+00:00

From Exploration to Production—Bridging the Deployment Gap for Deep Learning (Part 2)

2021-02-10T08:58:34+00:00

In this blogposts on deep learning model exploration, translation, and deployment we expand on the previous article with two additional approaches for model deployment: TensorFlow Serving and Docker as well as a rather hobbyist approach in which we build a simple web application that serves our model.

This is the second part of a series of two blogposts on deep learning model exploration, translation, and deployment. Both involve many technologies like PyTorch, Ten

From Exploration to Production—Bridging the Deployment Gap for Deep Learning (Part 2)2021-02-10T08:58:34+00:00

From Exploration to Production — Bridging the Deployment Gap for Deep Learning

2021-02-10T08:58:54+00:00

This article introduces EMNIST, we develop and train models with PyTorch, translate them with the Open Neural Network eXchange format ONNX and serve them through GraphPipe. We will orchestrate these technologies to solve the task of image classification using the more challenging and less popular EMNIST dataset.

This is the first part of a series of two blogposts on deep learning model exploration, translation, and deployment. Both involve many technologies like PyTorch, Tens

From Exploration to Production — Bridging the Deployment Gap for Deep Learning2021-02-10T08:58:54+00:00

Data Science in Production: Packaging, Versioning and Continuous Integration

2021-02-10T09:13:06+00:00

Here's what changes when your data science project grows from a proof of concept. How do you deploy your model, how can updates be rolled out, ...?

A common pattern in most data science projects I participated in is that it’s all fun and games until someone wants to put it into production. From that point in time

Data Science in Production: Packaging, Versioning and Continuous Integration2021-02-10T09:13:06+00:00