Chief Scientist of AI @ Pachyderm
Solutions Engineering Lead, North America @ Seldon
A complete end-to-end sentiment analysis pipeline from scratch including automated data labeling, model training, visualization, and more.
Join us for this webinar discussing the challenges that go into building enterprise-ready data pipelines for complex processes like natural language processing for sentiment analysis in financial services using Pachyderm, FinBert, and Seldon.
1. Building an enterprise-grade NLP Pipeline for financial services using Pachyderm, Seldon, and FinBert.
You’ll go through a complete end-to-end sentiment analysis pipeline from scratch including automated data labeling, model training, visualization, and more.
2. Combine multiple data sources into one workflow.
Models need constant tweaking and improvement. For that, we need to build a pipeline that is aware of new data, how to process it automatically in the most efficient manner possible.
3. Combine new human-labeled data via LabelStudio.
Configure our workflow so that we can automatically handle newly labeled data. This will ensure that the model never drifts in terms of accuracy
4. Automatically train a new model.
Using newly labeled data, we’ll train a new version of the model automatically.
5. Use data lineage to perform basic model validation.
You’ll explore how Pachyderms unique data lineage capabilities provide documentation that verifies exactly which data was used, when the model was created, and more.