There are a lot of high-value use cases for deep learning and image processing, such as manufacturing line inspections, cancer detection, and facial recognition for security. Despite the opportunity, organizations struggle to implement image analytics pipelines at scale for many reasons:
- Need expert knowledge to develop complex deep learning models
- Writing 1000s of lines of python code is time-consuming and prone to human error
- Debugging deep learning models is notoriously hard
- Parallelizing pipelines is a challenge, and takes weeks to train
Fortunately, Predibase—the leading low-code declarative ML platform—along with popular open-source project Ludwig make it easy to build a scalable deep learning pipeline for image analysis in < 15 lines of code.
In this webinar, we’ll deep dive into how to build an end-to-end image classification system using Predibase. We’ll start out by experimenting with different model architectures, analyze the model performance results to make sure that the performance is satisfactory, and finally, deploy the model into production.Join this webinar and step-by-step holiday-themed demo to learn how to:
- Improve and scale image processing with a modern data stack and example use cases,
- Use low-code declarative ML tools—like open-source Ludwig from Uber— and Predibase to simplify the development of complex deep learning pipelines, and
- Build, refine and deploy a state-of-the-art model to classify holiday images in <15 lines of code
