Building ML solutions from scratch is a challenge: complex low-level code and long dev cycles make it hard to deploy a single model in less than 6 mos. On the other hand, AutoML solutions lack flexibility and support for unstructured data, and typically don’t perform well for complex deep learning use cases.
Fortunately, declarative machine learning systems—like those started at Uber and Apple—provide a glass-box approach to automating ML that enables data teams to bring new models to market faster with flexibility and control.
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.
Watch the on-demand webinar to learn:
- About declarative ML systems incl. open-source Ludwig from Uber
- How to build state-of-the-art models in <15 lines of code with a config-driven approach
- How to rapidly train, iterate, and deploy a multimodal deep learning model with Predibase and Ludwig
